Overview

Dataset statistics

Number of variables103
Number of observations37548
Missing cells502441
Missing cells (%)13.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory171.6 MiB
Average record size in memory4.7 KiB

Variable types

Numeric23
URL1
Categorical78
Boolean1

Warnings

short_name has a high cardinality: 21976 distinct values High cardinality
long_name has a high cardinality: 23264 distinct values High cardinality
dob has a high cardinality: 6712 distinct values High cardinality
nationality has a high cardinality: 169 distinct values High cardinality
club has a high cardinality: 820 distinct values High cardinality
player_positions has a high cardinality: 1083 distinct values High cardinality
player_tags has a high cardinality: 109 distinct values High cardinality
loaned_from has a high cardinality: 435 distinct values High cardinality
joined has a high cardinality: 2189 distinct values High cardinality
player_traits has a high cardinality: 2310 distinct values High cardinality
attacking_crossing has a high cardinality: 721 distinct values High cardinality
attacking_finishing has a high cardinality: 771 distinct values High cardinality
attacking_heading_accuracy has a high cardinality: 662 distinct values High cardinality
attacking_short_passing has a high cardinality: 708 distinct values High cardinality
attacking_volleys has a high cardinality: 602 distinct values High cardinality
skill_dribbling has a high cardinality: 724 distinct values High cardinality
skill_curve has a high cardinality: 668 distinct values High cardinality
skill_fk_accuracy has a high cardinality: 568 distinct values High cardinality
skill_long_passing has a high cardinality: 759 distinct values High cardinality
skill_ball_control has a high cardinality: 713 distinct values High cardinality
movement_acceleration has a high cardinality: 976 distinct values High cardinality
movement_sprint_speed has a high cardinality: 981 distinct values High cardinality
movement_agility has a high cardinality: 888 distinct values High cardinality
movement_reactions has a high cardinality: 673 distinct values High cardinality
movement_balance has a high cardinality: 796 distinct values High cardinality
power_shot_power has a high cardinality: 671 distinct values High cardinality
power_jumping has a high cardinality: 850 distinct values High cardinality
power_stamina has a high cardinality: 1100 distinct values High cardinality
power_strength has a high cardinality: 927 distinct values High cardinality
power_long_shots has a high cardinality: 777 distinct values High cardinality
mentality_aggression has a high cardinality: 760 distinct values High cardinality
mentality_interceptions has a high cardinality: 761 distinct values High cardinality
mentality_positioning has a high cardinality: 721 distinct values High cardinality
mentality_vision has a high cardinality: 718 distinct values High cardinality
mentality_penalties has a high cardinality: 531 distinct values High cardinality
mentality_composure has a high cardinality: 1018 distinct values High cardinality
defending_marking has a high cardinality: 804 distinct values High cardinality
defending_standing_tackle has a high cardinality: 764 distinct values High cardinality
defending_sliding_tackle has a high cardinality: 727 distinct values High cardinality
goalkeeping_diving has a high cardinality: 266 distinct values High cardinality
goalkeeping_handling has a high cardinality: 268 distinct values High cardinality
goalkeeping_kicking has a high cardinality: 267 distinct values High cardinality
goalkeeping_positioning has a high cardinality: 276 distinct values High cardinality
goalkeeping_reflexes has a high cardinality: 269 distinct values High cardinality
ls has a high cardinality: 187 distinct values High cardinality
st has a high cardinality: 187 distinct values High cardinality
rs has a high cardinality: 187 distinct values High cardinality
lw has a high cardinality: 210 distinct values High cardinality
lf has a high cardinality: 201 distinct values High cardinality
cf has a high cardinality: 201 distinct values High cardinality
rf has a high cardinality: 201 distinct values High cardinality
rw has a high cardinality: 210 distinct values High cardinality
lam has a high cardinality: 206 distinct values High cardinality
cam has a high cardinality: 206 distinct values High cardinality
ram has a high cardinality: 206 distinct values High cardinality
lm has a high cardinality: 199 distinct values High cardinality
lcm has a high cardinality: 184 distinct values High cardinality
cm has a high cardinality: 184 distinct values High cardinality
rcm has a high cardinality: 184 distinct values High cardinality
rm has a high cardinality: 199 distinct values High cardinality
lwb has a high cardinality: 188 distinct values High cardinality
ldm has a high cardinality: 198 distinct values High cardinality
cdm has a high cardinality: 198 distinct values High cardinality
rdm has a high cardinality: 198 distinct values High cardinality
rwb has a high cardinality: 188 distinct values High cardinality
lb has a high cardinality: 188 distinct values High cardinality
lcb has a high cardinality: 210 distinct values High cardinality
cb has a high cardinality: 210 distinct values High cardinality
rcb has a high cardinality: 210 distinct values High cardinality
rb has a high cardinality: 188 distinct values High cardinality
sofifa_id is highly correlated with ageHigh correlation
age is highly correlated with sofifa_id and 1 other fieldsHigh correlation
height_cm is highly correlated with weight_kgHigh correlation
weight_kg is highly correlated with height_cm and 1 other fieldsHigh correlation
overall is highly correlated with potential and 11 other fieldsHigh correlation
potential is highly correlated with overall and 7 other fieldsHigh correlation
value is highly correlated with overall and 7 other fieldsHigh correlation
international_reputation is highly correlated with overall and 2 other fieldsHigh correlation
skill_moves is highly correlated with shooting and 2 other fieldsHigh correlation
release_clause_eur is highly correlated with overall and 6 other fieldsHigh correlation
pace is highly correlated with dribblingHigh correlation
shooting is highly correlated with skill_moves and 2 other fieldsHigh correlation
passing is highly correlated with overall and 3 other fieldsHigh correlation
dribbling is highly correlated with overall and 4 other fieldsHigh correlation
defending is highly correlated with physicHigh correlation
physic is highly correlated with weight_kg and 2 other fieldsHigh correlation
gk_diving is highly correlated with overall and 7 other fieldsHigh correlation
gk_handling is highly correlated with overall and 7 other fieldsHigh correlation
gk_kicking is highly correlated with overall and 5 other fieldsHigh correlation
gk_reflexes is highly correlated with overall and 7 other fieldsHigh correlation
gk_positioning is highly correlated with age and 7 other fieldsHigh correlation
sofifa_id is highly correlated with age and 5 other fieldsHigh correlation
age is highly correlated with sofifa_id and 2 other fieldsHigh correlation
height_cm is highly correlated with weight_kgHigh correlation
weight_kg is highly correlated with height_cm and 1 other fieldsHigh correlation
overall is highly correlated with sofifa_id and 11 other fieldsHigh correlation
potential is highly correlated with overall and 7 other fieldsHigh correlation
value is highly correlated with overall and 9 other fieldsHigh correlation
skill_moves is highly correlated with shooting and 2 other fieldsHigh correlation
release_clause_eur is highly correlated with overall and 9 other fieldsHigh correlation
pace is highly correlated with dribblingHigh correlation
shooting is highly correlated with skill_moves and 2 other fieldsHigh correlation
passing is highly correlated with overall and 5 other fieldsHigh correlation
dribbling is highly correlated with overall and 6 other fieldsHigh correlation
defending is highly correlated with physicHigh correlation
physic is highly correlated with weight_kg and 2 other fieldsHigh correlation
gk_diving is highly correlated with sofifa_id and 8 other fieldsHigh correlation
gk_handling is highly correlated with sofifa_id and 9 other fieldsHigh correlation
gk_kicking is highly correlated with overall and 7 other fieldsHigh correlation
gk_reflexes is highly correlated with sofifa_id and 8 other fieldsHigh correlation
gk_positioning is highly correlated with sofifa_id and 9 other fieldsHigh correlation
sofifa_id is highly correlated with ageHigh correlation
age is highly correlated with sofifa_idHigh correlation
height_cm is highly correlated with weight_kgHigh correlation
weight_kg is highly correlated with height_cmHigh correlation
overall is highly correlated with value and 7 other fieldsHigh correlation
potential is highly correlated with value and 3 other fieldsHigh correlation
value is highly correlated with overall and 6 other fieldsHigh correlation
skill_moves is highly correlated with shooting and 1 other fieldsHigh correlation
release_clause_eur is highly correlated with overall and 6 other fieldsHigh correlation
shooting is highly correlated with skill_moves and 1 other fieldsHigh correlation
passing is highly correlated with overall and 1 other fieldsHigh correlation
dribbling is highly correlated with skill_moves and 2 other fieldsHigh correlation
gk_diving is highly correlated with overall and 7 other fieldsHigh correlation
gk_handling is highly correlated with overall and 6 other fieldsHigh correlation
gk_kicking is highly correlated with overall and 4 other fieldsHigh correlation
gk_reflexes is highly correlated with overall and 7 other fieldsHigh correlation
gk_positioning is highly correlated with overall and 6 other fieldsHigh correlation
body_type is highly correlated with overall and 5 other fieldsHigh correlation
potential is highly correlated with gk_positioning and 12 other fieldsHigh correlation
gk_positioning is highly correlated with potential and 11 other fieldsHigh correlation
gk_diving is highly correlated with potential and 11 other fieldsHigh correlation
gk_handling is highly correlated with potential and 10 other fieldsHigh correlation
overall is highly correlated with body_type and 17 other fieldsHigh correlation
international_reputation is highly correlated with body_type and 13 other fieldsHigh correlation
defending is highly correlated with potential and 6 other fieldsHigh correlation
gk_kicking is highly correlated with potential and 7 other fieldsHigh correlation
team_position is highly correlated with gk_positioning and 5 other fieldsHigh correlation
height_cm is highly correlated with weight_kg and 1 other fieldsHigh correlation
real_face is highly correlated with gk_positioning and 4 other fieldsHigh correlation
weight_kg is highly correlated with body_type and 2 other fieldsHigh correlation
shooting is highly correlated with potential and 9 other fieldsHigh correlation
sofifa_id is highly correlated with ageHigh correlation
physic is highly correlated with overall and 2 other fieldsHigh correlation
value is highly correlated with body_type and 12 other fieldsHigh correlation
gk_reflexes is highly correlated with potential and 11 other fieldsHigh correlation
pace is highly correlated with dribblingHigh correlation
skill_moves is highly correlated with overall and 6 other fieldsHigh correlation
nation_position is highly correlated with body_type and 7 other fieldsHigh correlation
passing is highly correlated with potential and 7 other fieldsHigh correlation
age is highly correlated with gk_positioning and 6 other fieldsHigh correlation
dribbling is highly correlated with potential and 9 other fieldsHigh correlation
release_clause_eur is highly correlated with body_type and 12 other fieldsHigh correlation
release_clause_eur has 3020 (8.0%) missing values Missing
player_tags has 34410 (91.6%) missing values Missing
team_position has 502 (1.3%) missing values Missing
team_jersey_number has 502 (1.3%) missing values Missing
loaned_from has 35051 (93.3%) missing values Missing
joined has 2999 (8.0%) missing values Missing
contract_valid_until has 502 (1.3%) missing values Missing
nation_position has 35147 (93.6%) missing values Missing
nation_jersey_number has 35147 (93.6%) missing values Missing
pace has 4192 (11.2%) missing values Missing
shooting has 4192 (11.2%) missing values Missing
passing has 4192 (11.2%) missing values Missing
dribbling has 4192 (11.2%) missing values Missing
defending has 4192 (11.2%) missing values Missing
physic has 4192 (11.2%) missing values Missing
gk_diving has 33356 (88.8%) missing values Missing
gk_handling has 33356 (88.8%) missing values Missing
gk_kicking has 33356 (88.8%) missing values Missing
gk_reflexes has 33356 (88.8%) missing values Missing
gk_speed has 33356 (88.8%) missing values Missing
gk_positioning has 33356 (88.8%) missing values Missing
player_traits has 20881 (55.6%) missing values Missing
ls has 4192 (11.2%) missing values Missing
st has 4192 (11.2%) missing values Missing
rs has 4192 (11.2%) missing values Missing
lw has 4192 (11.2%) missing values Missing
lf has 4192 (11.2%) missing values Missing
cf has 4192 (11.2%) missing values Missing
rf has 4192 (11.2%) missing values Missing
rw has 4192 (11.2%) missing values Missing
lam has 4192 (11.2%) missing values Missing
cam has 4192 (11.2%) missing values Missing
ram has 4192 (11.2%) missing values Missing
lm has 4192 (11.2%) missing values Missing
lcm has 4192 (11.2%) missing values Missing
cm has 4192 (11.2%) missing values Missing
rcm has 4192 (11.2%) missing values Missing
rm has 4192 (11.2%) missing values Missing
lwb has 4192 (11.2%) missing values Missing
ldm has 4192 (11.2%) missing values Missing
cdm has 4192 (11.2%) missing values Missing
rdm has 4192 (11.2%) missing values Missing
rwb has 4192 (11.2%) missing values Missing
lb has 4192 (11.2%) missing values Missing
lcb has 4192 (11.2%) missing values Missing
cb has 4192 (11.2%) missing values Missing
rcb has 4192 (11.2%) missing values Missing
rb has 4192 (11.2%) missing values Missing
short_name is uniformly distributed Uniform
long_name is uniformly distributed Uniform
player_url has unique values Unique
value has 523 (1.4%) zeros Zeros

Reproduction

Analysis started2022-01-13 08:43:13.223218
Analysis finished2022-01-13 08:44:03.710637
Duration50.49 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

sofifa_id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct23145
Distinct (%)61.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean213684.4469
Minimum164
Maximum252905
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size293.5 KiB
2022-01-13T14:14:03.826759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum164
5-th percentile160309
Q1199510.75
median221168
Q3235896.25
95-th percentile245717.65
Maximum252905
Range252741
Interquartile range (IQR)36385.5

Descriptive statistics

Standard deviation30601.73451
Coefficient of variation (CV)0.1432099292
Kurtosis8.90889894
Mean213684.4469
Median Absolute Deviation (MAD)17309.5
Skewness-2.167527018
Sum8023423612
Variance936466155.2
MonotonicityNot monotonic
2022-01-13T14:14:03.983737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1964323
 
< 0.1%
2055533
 
< 0.1%
1884143
 
< 0.1%
2055853
 
< 0.1%
2301813
 
< 0.1%
2342793
 
< 0.1%
1892253
 
< 0.1%
2076643
 
< 0.1%
2220413
 
< 0.1%
1913363
 
< 0.1%
Other values (23135)37518
99.9%
ValueCountFrequency (%)
1641
 
< 0.1%
5911
 
< 0.1%
6572
< 0.1%
7682
< 0.1%
11781
 
< 0.1%
11793
< 0.1%
12101
 
< 0.1%
21472
< 0.1%
23353
< 0.1%
27023
< 0.1%
ValueCountFrequency (%)
2529051
< 0.1%
2529041
< 0.1%
2529031
< 0.1%
2529001
< 0.1%
2528991
< 0.1%
2528981
< 0.1%
2528931
< 0.1%
2528921
< 0.1%
2528901
< 0.1%
2528881
< 0.1%

player_url
URL

UNIQUE

Distinct37548
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
https://sofifa.com/player/245144/franco-ferrari/19/159222
 
1
https://sofifa.com/player/209675/janoi-donacien/18/158855
 
1
https://sofifa.com/player/219984/mattia-aramu/18/158855
 
1
https://sofifa.com/player/229998/dominik-schad/20/159586
 
1
https://sofifa.com/player/222078/bradford-jamieson-iv/18/158855
 
1
Other values (37543)
37543 
ValueCountFrequency (%)
https://sofifa.com/player/245144/franco-ferrari/19/1592221
 
< 0.1%
https://sofifa.com/player/209675/janoi-donacien/18/1588551
 
< 0.1%
https://sofifa.com/player/219984/mattia-aramu/18/1588551
 
< 0.1%
https://sofifa.com/player/229998/dominik-schad/20/1595861
 
< 0.1%
https://sofifa.com/player/222078/bradford-jamieson-iv/18/1588551
 
< 0.1%
https://sofifa.com/player/157797/luca-ceccarelli/18/1588551
 
< 0.1%
https://sofifa.com/player/222396/dominic-ball/18/1588551
 
< 0.1%
https://sofifa.com/player/212126/nikola-ninkovic/19/1592221
 
< 0.1%
https://sofifa.com/player/215202/lucas-janson/18/1588551
 
< 0.1%
https://sofifa.com/player/219625/facundo-cardozo/18/1588551
 
< 0.1%
Other values (37538)37538
> 99.9%
ValueCountFrequency (%)
https37548
100.0%
ValueCountFrequency (%)
sofifa.com37548
100.0%
ValueCountFrequency (%)
/player/239468/rahman-bugra-cagiran/19/1592221
 
< 0.1%
/player/210429/bright-addae/20/1595861
 
< 0.1%
/player/199152/dejan-lekic/18/1588551
 
< 0.1%
/player/229993/halil-ibrahim-sonmez/19/1592221
 
< 0.1%
/player/152910/cristian-nasuti/18/1588551
 
< 0.1%
/player/187208/alfredo-saldivar/19/1592221
 
< 0.1%
/player/204709/diego-rubio/18/1588551
 
< 0.1%
/player/229718/will-patching/19/1592221
 
< 0.1%
/player/209002/mande-sayouba/18/1588551
 
< 0.1%
/player/242952/sergio-bareiro/19/1592221
 
< 0.1%
Other values (37538)37538
> 99.9%
ValueCountFrequency (%)
37548
100.0%
ValueCountFrequency (%)
37548
100.0%

short_name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct21976
Distinct (%)58.5%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
J. Rodríguez
 
20
J. Williams
 
13
M. Smith
 
13
J. Hernández
 
13
Paulinho
 
13
Other values (21971)
37476 

Length

Max length22
Median length10
Mean length10.03230532
Min length2

Characters and Unicode

Total characters376693
Distinct characters140
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11222 ?
Unique (%)29.9%

Sample

1st rowY. Tielemans
2nd rowL. Mareček
3rd rowF. Forestieri
4th rowR. Maniero
5th rowR. Borges

Common Values

ValueCountFrequency (%)
J. Rodríguez20
 
0.1%
J. Williams13
 
< 0.1%
M. Smith13
 
< 0.1%
J. Hernández13
 
< 0.1%
Paulinho13
 
< 0.1%
J. Valencia13
 
< 0.1%
Felipe12
 
< 0.1%
R. Williams12
 
< 0.1%
J. Martínez11
 
< 0.1%
J. Jones11
 
< 0.1%
Other values (21966)37417
99.7%

Length

2022-01-13T14:14:04.216467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
m3725
 
4.9%
j3309
 
4.4%
a3293
 
4.4%
s2103
 
2.8%
d1963
 
2.6%
l1654
 
2.2%
r1573
 
2.1%
c1570
 
2.1%
t1274
 
1.7%
f1233
 
1.6%
Other values (16952)53652
71.2%

Most occurring characters

ValueCountFrequency (%)
37801
 
10.0%
.31514
 
8.4%
a29165
 
7.7%
e23937
 
6.4%
o20295
 
5.4%
i19695
 
5.2%
n19075
 
5.1%
r18546
 
4.9%
l13710
 
3.6%
s11303
 
3.0%
Other values (130)151652
40.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter230964
61.3%
Uppercase Letter75889
 
20.1%
Space Separator37801
 
10.0%
Other Punctuation31709
 
8.4%
Dash Punctuation326
 
0.1%
Other Symbol4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a29165
12.6%
e23937
10.4%
o20295
 
8.8%
i19695
 
8.5%
n19075
 
8.3%
r18546
 
8.0%
l13710
 
5.9%
s11303
 
4.9%
u8810
 
3.8%
t8439
 
3.7%
Other values (71)57989
25.1%
Uppercase Letter
ValueCountFrequency (%)
M7756
 
10.2%
A6310
 
8.3%
S5918
 
7.8%
J4953
 
6.5%
B4481
 
5.9%
C4440
 
5.9%
D4247
 
5.6%
L3745
 
4.9%
R3654
 
4.8%
G3366
 
4.4%
Other values (44)27019
35.6%
Other Punctuation
ValueCountFrequency (%)
.31514
99.4%
'195
 
0.6%
Space Separator
ValueCountFrequency (%)
37801
100.0%
Dash Punctuation
ValueCountFrequency (%)
-326
100.0%
Other Symbol
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin306853
81.5%
Common69840
 
18.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a29165
 
9.5%
e23937
 
7.8%
o20295
 
6.6%
i19695
 
6.4%
n19075
 
6.2%
r18546
 
6.0%
l13710
 
4.5%
s11303
 
3.7%
u8810
 
2.9%
t8439
 
2.8%
Other values (125)133878
43.6%
Common
ValueCountFrequency (%)
37801
54.1%
.31514
45.1%
-326
 
0.5%
'195
 
0.3%
4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII369697
98.1%
Latin 1 Sup5271
 
1.4%
Latin Ext A1673
 
0.4%
Latin Ext B48
 
< 0.1%
Specials4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
37801
 
10.2%
.31514
 
8.5%
a29165
 
7.9%
e23937
 
6.5%
o20295
 
5.5%
i19695
 
5.3%
n19075
 
5.2%
r18546
 
5.0%
l13710
 
3.7%
s11303
 
3.1%
Other values (46)144656
39.1%
Latin Ext A
ValueCountFrequency (%)
ć639
38.2%
ı149
 
8.9%
š125
 
7.5%
č120
 
7.2%
ń74
 
4.4%
ş73
 
4.4%
ă66
 
3.9%
ł60
 
3.6%
ğ51
 
3.0%
Š50
 
3.0%
Other values (28)266
15.9%
Latin 1 Sup
ValueCountFrequency (%)
é982
18.6%
á889
16.9%
í774
14.7%
ó415
7.9%
ñ287
 
5.4%
ü271
 
5.1%
ö263
 
5.0%
ú180
 
3.4%
ã157
 
3.0%
ø156
 
3.0%
Other values (31)897
17.0%
Latin Ext B
ValueCountFrequency (%)
ș24
50.0%
ț19
39.6%
Ș4
 
8.3%
Ț1
 
2.1%
Specials
ValueCountFrequency (%)
4
100.0%

long_name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct23264
Distinct (%)62.0%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
Scott Brown
 
7
Adama Traoré
 
7
Danny Ward
 
6
Tommy Smith
 
6
Michał Nalepa
 
6
Other values (23259)
37516 

Length

Max length49
Median length15
Mean length15.98881432
Min length2

Characters and Unicode

Total characters600348
Distinct characters1275
Distinct categories9 ?
Distinct scripts5 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12332 ?
Unique (%)32.8%

Sample

1st rowYouri Tielemans
2nd rowLukáš Mareček
3rd rowFernando Martín Forestieri
4th rowRiccardo Maniero
5th rowRowllin Borges

Common Values

ValueCountFrequency (%)
Scott Brown7
 
< 0.1%
Adama Traoré7
 
< 0.1%
Danny Ward6
 
< 0.1%
Tommy Smith6
 
< 0.1%
Michał Nalepa6
 
< 0.1%
Greg Taylor6
 
< 0.1%
Michael Smith6
 
< 0.1%
Lisandro López5
 
< 0.1%
Adam Smith5
 
< 0.1%
Michael López5
 
< 0.1%
Other values (23254)37489
99.8%

Length

2022-01-13T14:14:04.412260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de524
 
0.6%
al483
 
0.5%
daniel455
 
0.5%
josé429
 
0.5%
david396
 
0.4%
silva380
 
0.4%
carlos345
 
0.4%
da323
 
0.4%
juan300
 
0.3%
santos247
 
0.3%
Other values (23362)86744
95.7%

Most occurring characters

ValueCountFrequency (%)
a58478
 
9.7%
53549
 
8.9%
e46369
 
7.7%
o38979
 
6.5%
i38817
 
6.5%
r37991
 
6.3%
n37647
 
6.3%
l27260
 
4.5%
s23385
 
3.9%
t16343
 
2.7%
Other values (1265)221530
36.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter449926
74.9%
Uppercase Letter87024
 
14.5%
Space Separator53552
 
8.9%
Other Letter8925
 
1.5%
Dash Punctuation566
 
0.1%
Other Punctuation328
 
0.1%
Modifier Letter19
 
< 0.1%
Final Punctuation6
 
< 0.1%
Format2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
142
 
1.6%
139
 
1.6%
127
 
1.4%
124
 
1.4%
121
 
1.4%
119
 
1.3%
83
 
0.9%
65
 
0.7%
61
 
0.7%
59
 
0.7%
Other values (1114)7885
88.3%
Lowercase Letter
ValueCountFrequency (%)
a58478
13.0%
e46369
10.3%
o38979
 
8.7%
i38817
 
8.6%
r37991
 
8.4%
n37647
 
8.4%
l27260
 
6.1%
s23385
 
5.2%
t16343
 
3.6%
u15621
 
3.5%
Other values (75)109036
24.2%
Uppercase Letter
ValueCountFrequency (%)
M9265
 
10.6%
A8144
 
9.4%
S6704
 
7.7%
J5649
 
6.5%
C5321
 
6.1%
B5037
 
5.8%
D4843
 
5.6%
R4533
 
5.2%
L4170
 
4.8%
G4073
 
4.7%
Other values (45)29285
33.7%
Other Punctuation
ValueCountFrequency (%)
'236
72.0%
.73
 
22.3%
·12
 
3.7%
7
 
2.1%
Space Separator
ValueCountFrequency (%)
53549
> 99.9%
 3
 
< 0.1%
Modifier Letter
ValueCountFrequency (%)
15
78.9%
4
 
21.1%
Dash Punctuation
ValueCountFrequency (%)
-566
100.0%
Final Punctuation
ValueCountFrequency (%)
6
100.0%
Format
ValueCountFrequency (%)
­2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin536950
89.4%
Common54469
 
9.1%
Han6761
 
1.1%
Hangul2068
 
0.3%
Katakana100
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
142
 
2.1%
127
 
1.9%
124
 
1.8%
121
 
1.8%
83
 
1.2%
65
 
1.0%
61
 
0.9%
59
 
0.9%
58
 
0.9%
56
 
0.8%
Other values (921)5865
86.7%
Hangul
ValueCountFrequency (%)
139
 
6.7%
119
 
5.8%
59
 
2.9%
56
 
2.7%
54
 
2.6%
53
 
2.6%
51
 
2.5%
48
 
2.3%
48
 
2.3%
47
 
2.3%
Other values (147)1394
67.4%
Latin
ValueCountFrequency (%)
a58478
 
10.9%
e46369
 
8.6%
o38979
 
7.3%
i38817
 
7.2%
r37991
 
7.1%
n37647
 
7.0%
l27260
 
5.1%
s23385
 
4.4%
t16343
 
3.0%
u15621
 
2.9%
Other values (130)196060
36.5%
Katakana
ValueCountFrequency (%)
8
 
8.0%
7
 
7.0%
5
 
5.0%
4
 
4.0%
4
 
4.0%
4
 
4.0%
4
 
4.0%
4
 
4.0%
4
 
4.0%
4
 
4.0%
Other values (27)52
52.0%
Common
ValueCountFrequency (%)
53549
98.3%
-566
 
1.0%
'236
 
0.4%
.73
 
0.1%
15
 
< 0.1%
·12
 
< 0.1%
7
 
< 0.1%
6
 
< 0.1%
 3
 
< 0.1%
­2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII577815
96.2%
Latin 1 Sup11306
 
1.9%
CJK6754
 
1.1%
Latin Ext A2190
 
0.4%
Hangul2068
 
0.3%
Katakana122
 
< 0.1%
Latin Ext B79
 
< 0.1%
Punctuation6
 
< 0.1%
None4
 
< 0.1%
CJK Compat Ideographs3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a58478
 
10.1%
53549
 
9.3%
e46369
 
8.0%
o38979
 
6.7%
i38817
 
6.7%
r37991
 
6.6%
n37647
 
6.5%
l27260
 
4.7%
s23385
 
4.0%
t16343
 
2.8%
Other values (46)198997
34.4%
Latin 1 Sup
ValueCountFrequency (%)
é2544
22.5%
á2338
20.7%
í1932
17.1%
ó851
 
7.5%
ú460
 
4.1%
ñ448
 
4.0%
ö322
 
2.8%
ü308
 
2.7%
Á302
 
2.7%
ã253
 
2.2%
Other values (35)1548
13.7%
Latin Ext A
ValueCountFrequency (%)
ć654
29.9%
š242
 
11.1%
ł184
 
8.4%
ı178
 
8.1%
č122
 
5.6%
ğ97
 
4.4%
ă96
 
4.4%
ş88
 
4.0%
ń74
 
3.4%
Š55
 
2.5%
Other values (29)400
18.3%
CJK
ValueCountFrequency (%)
142
 
2.1%
127
 
1.9%
124
 
1.8%
121
 
1.8%
83
 
1.2%
65
 
1.0%
61
 
0.9%
59
 
0.9%
58
 
0.9%
56
 
0.8%
Other values (919)5858
86.7%
Hangul
ValueCountFrequency (%)
139
 
6.7%
119
 
5.8%
59
 
2.9%
56
 
2.7%
54
 
2.6%
53
 
2.6%
51
 
2.5%
48
 
2.3%
48
 
2.3%
47
 
2.3%
Other values (147)1394
67.4%
Katakana
ValueCountFrequency (%)
15
 
12.3%
8
 
6.6%
7
 
5.7%
7
 
5.7%
5
 
4.1%
4
 
3.3%
4
 
3.3%
4
 
3.3%
4
 
3.3%
4
 
3.3%
Other values (29)60
49.2%
Latin Ext B
ValueCountFrequency (%)
ț36
45.6%
ș30
38.0%
Ș9
 
11.4%
ư2
 
2.5%
Ț1
 
1.3%
ơ1
 
1.3%
CJK Compat Ideographs
ValueCountFrequency (%)
3
100.0%
None
ValueCountFrequency (%)
4
100.0%
Punctuation
ValueCountFrequency (%)
6
100.0%
Latin Ext Additional
ValueCountFrequency (%)
1
100.0%

age
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct30
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.21372643
Minimum16
Maximum47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size293.5 KiB
2022-01-13T14:14:04.497481image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile18
Q121
median25
Q329
95-th percentile33
Maximum47
Range31
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.655780807
Coefficient of variation (CV)0.1846526264
Kurtosis-0.4769181458
Mean25.21372643
Median Absolute Deviation (MAD)4
Skewness0.3930683822
Sum946725
Variance21.67629492
MonotonicityNot monotonic
2022-01-13T14:14:04.574716image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
252865
 
7.6%
222841
 
7.6%
232825
 
7.5%
212820
 
7.5%
242763
 
7.4%
262657
 
7.1%
202538
 
6.8%
272480
 
6.6%
282261
 
6.0%
192131
 
5.7%
Other values (20)11367
30.3%
ValueCountFrequency (%)
1634
 
0.1%
17530
 
1.4%
181382
3.7%
192131
5.7%
202538
6.8%
212820
7.5%
222841
7.6%
232825
7.5%
242763
7.4%
252865
7.6%
ValueCountFrequency (%)
471
 
< 0.1%
442
 
< 0.1%
432
 
< 0.1%
422
 
< 0.1%
418
 
< 0.1%
4027
 
0.1%
3952
 
0.1%
3883
 
0.2%
37168
0.4%
36280
0.7%

dob
Categorical

HIGH CARDINALITY

Distinct6712
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
1992-02-29
 
319
1984-02-29
 
314
1988-02-29
 
286
1991-01-08
 
30
1997-01-09
 
28
Other values (6707)
36571 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters375480
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique908 ?
Unique (%)2.4%

Sample

1st row1997-05-07
2nd row1990-04-17
3rd row1990-01-15
4th row1987-11-26
5th row1992-06-05

Common Values

ValueCountFrequency (%)
1992-02-29319
 
0.8%
1984-02-29314
 
0.8%
1988-02-29286
 
0.8%
1991-01-0830
 
0.1%
1997-01-0928
 
0.1%
1997-01-0126
 
0.1%
1993-03-0524
 
0.1%
1993-01-1524
 
0.1%
1995-04-1924
 
0.1%
1996-11-1124
 
0.1%
Other values (6702)36449
97.1%

Length

2022-01-13T14:14:04.686928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1992-02-29319
 
0.8%
1984-02-29314
 
0.8%
1988-02-29286
 
0.8%
1991-01-0830
 
0.1%
1997-01-0928
 
0.1%
1997-01-0126
 
0.1%
1993-03-0524
 
0.1%
1993-01-1524
 
0.1%
1995-04-1924
 
0.1%
1996-11-1124
 
0.1%
Other values (6702)36449
97.1%

Most occurring characters

ValueCountFrequency (%)
-75096
20.0%
972775
19.4%
169372
18.5%
054294
14.5%
227780
 
7.4%
821406
 
5.7%
312213
 
3.3%
711073
 
2.9%
510658
 
2.8%
410425
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number300384
80.0%
Dash Punctuation75096
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
972775
24.2%
169372
23.1%
054294
18.1%
227780
 
9.2%
821406
 
7.1%
312213
 
4.1%
711073
 
3.7%
510658
 
3.5%
410425
 
3.5%
610388
 
3.5%
Dash Punctuation
ValueCountFrequency (%)
-75096
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common375480
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
-75096
20.0%
972775
19.4%
169372
18.5%
054294
14.5%
227780
 
7.4%
821406
 
5.7%
312213
 
3.3%
711073
 
2.9%
510658
 
2.8%
410425
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII375480
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
-75096
20.0%
972775
19.4%
169372
18.5%
054294
14.5%
227780
 
7.4%
821406
 
5.7%
312213
 
3.3%
711073
 
2.9%
510658
 
2.8%
410425
 
2.8%

height_cm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct51
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean181.3008416
Minimum154
Maximum205
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size293.5 KiB
2022-01-13T14:14:04.736330image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum154
5-th percentile170
Q1177
median181
Q3186
95-th percentile192
Maximum205
Range51
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.718944194
Coefficient of variation (CV)0.0370596415
Kurtosis-0.261361742
Mean181.3008416
Median Absolute Deviation (MAD)5
Skewness-0.04820485017
Sum6807484
Variance45.14421108
MonotonicityNot monotonic
2022-01-13T14:14:04.789923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1803169
 
8.4%
1782502
 
6.7%
1832485
 
6.6%
1852461
 
6.6%
1752088
 
5.6%
1881823
 
4.9%
1821770
 
4.7%
1841676
 
4.5%
1861530
 
4.1%
1871463
 
3.9%
Other values (41)16581
44.2%
ValueCountFrequency (%)
1541
 
< 0.1%
1552
 
< 0.1%
1562
 
< 0.1%
1573
 
< 0.1%
1588
 
< 0.1%
1597
 
< 0.1%
16014
 
< 0.1%
16114
 
< 0.1%
16218
 
< 0.1%
16348
0.1%
ValueCountFrequency (%)
2053
 
< 0.1%
2039
 
< 0.1%
2028
 
< 0.1%
20121
 
0.1%
20018
 
< 0.1%
19926
 
0.1%
19873
 
0.2%
197110
0.3%
196274
0.7%
195230
0.6%

weight_kg
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct58
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.29090764
Minimum49
Maximum110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size293.5 KiB
2022-01-13T14:14:04.841178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum49
5-th percentile64
Q170
median75
Q380
95-th percentile87
Maximum110
Range61
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.024779156
Coefficient of variation (CV)0.0933018259
Kurtosis0.07666251427
Mean75.29090764
Median Absolute Deviation (MAD)5
Skewness0.2128692037
Sum2827023
Variance49.3475222
MonotonicityNot monotonic
2022-01-13T14:14:04.892267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
753005
 
8.0%
702956
 
7.9%
802165
 
5.8%
782024
 
5.4%
721995
 
5.3%
731962
 
5.2%
741849
 
4.9%
771819
 
4.8%
761784
 
4.8%
791425
 
3.8%
Other values (48)16564
44.1%
ValueCountFrequency (%)
491
 
< 0.1%
503
 
< 0.1%
525
 
< 0.1%
537
 
< 0.1%
5412
 
< 0.1%
5517
 
< 0.1%
5630
0.1%
5732
0.1%
5874
0.2%
5965
0.2%
ValueCountFrequency (%)
1103
 
< 0.1%
1073
 
< 0.1%
1061
 
< 0.1%
1041
 
< 0.1%
1034
 
< 0.1%
1027
 
< 0.1%
1017
 
< 0.1%
1005
 
< 0.1%
995
 
< 0.1%
9818
< 0.1%

nationality
Categorical

HIGH CARDINALITY

Distinct169
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
England
3471 
Germany
 
2508
Spain
 
2185
France
 
1986
Argentina
 
1958
Other values (164)
25440 

Length

Max length20
Median length7
Mean length7.621897305
Min length4

Characters and Unicode

Total characters286187
Distinct characters57
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)< 0.1%

Sample

1st rowBelgium
2nd rowCzech Republic
3rd rowItaly
4th rowItaly
5th rowIndia

Common Values

ValueCountFrequency (%)
England3471
 
9.2%
Germany2508
 
6.7%
Spain2185
 
5.8%
France1986
 
5.3%
Argentina1958
 
5.2%
Brazil1723
 
4.6%
Italy1590
 
4.2%
Colombia1213
 
3.2%
Japan968
 
2.6%
Netherlands877
 
2.3%
Other values (159)19069
50.8%

Length

2022-01-13T14:14:05.012389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
england3471
 
8.0%
germany2508
 
5.8%
spain2185
 
5.1%
france1986
 
4.6%
argentina1958
 
4.5%
brazil1723
 
4.0%
republic1689
 
3.9%
italy1590
 
3.7%
colombia1213
 
2.8%
japan968
 
2.2%
Other values (190)23917
55.4%

Most occurring characters

ValueCountFrequency (%)
a38373
 
13.4%
n28391
 
9.9%
e22892
 
8.0%
r19478
 
6.8%
i19153
 
6.7%
l17531
 
6.1%
o11292
 
3.9%
t10548
 
3.7%
d10021
 
3.5%
g8561
 
3.0%
Other values (47)99947
34.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter237348
82.9%
Uppercase Letter43144
 
15.1%
Space Separator5660
 
2.0%
Other Punctuation35
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a38373
16.2%
n28391
12.0%
e22892
9.6%
r19478
 
8.2%
i19153
 
8.1%
l17531
 
7.4%
o11292
 
4.8%
t10548
 
4.4%
d10021
 
4.2%
g8561
 
3.6%
Other values (19)51108
21.5%
Uppercase Letter
ValueCountFrequency (%)
S6332
14.7%
A4142
9.6%
C3808
8.8%
E3686
8.5%
G3211
 
7.4%
R3053
 
7.1%
I3009
 
7.0%
B2666
 
6.2%
P2217
 
5.1%
F2213
 
5.1%
Other values (15)8807
20.4%
Other Punctuation
ValueCountFrequency (%)
&29
82.9%
.6
 
17.1%
Space Separator
ValueCountFrequency (%)
5660
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin280492
98.0%
Common5695
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a38373
13.7%
n28391
 
10.1%
e22892
 
8.2%
r19478
 
6.9%
i19153
 
6.8%
l17531
 
6.3%
o11292
 
4.0%
t10548
 
3.8%
d10021
 
3.6%
g8561
 
3.1%
Other values (44)94252
33.6%
Common
ValueCountFrequency (%)
5660
99.4%
&29
 
0.5%
.6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII286181
> 99.9%
Latin 1 Sup6
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a38373
 
13.4%
n28391
 
9.9%
e22892
 
8.0%
r19478
 
6.8%
i19153
 
6.7%
l17531
 
6.1%
o11292
 
3.9%
t10548
 
3.7%
d10021
 
3.5%
g8561
 
3.0%
Other values (44)99941
34.9%
Latin 1 Sup
ValueCountFrequency (%)
ã2
33.3%
é2
33.3%
í2
33.3%

club
Categorical

HIGH CARDINALITY

Distinct820
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
Borussia Dortmund
 
81
AS Monaco
 
78
TSG 1899 Hoffenheim
 
75
Newcastle United
 
75
Sheffield Wednesday
 
75
Other values (815)
37164 

Length

Max length36
Median length13
Mean length13.22235539
Min length3

Characters and Unicode

Total characters496473
Distinct characters105
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)< 0.1%

Sample

1st rowAS Monaco
2nd rowSporting Lokeren
3rd rowSheffield Wednesday
4th rowPescara
5th rowIndia

Common Values

ValueCountFrequency (%)
Borussia Dortmund81
 
0.2%
AS Monaco78
 
0.2%
TSG 1899 Hoffenheim75
 
0.2%
Newcastle United75
 
0.2%
Sheffield Wednesday75
 
0.2%
Burnley74
 
0.2%
Brighton & Hove Albion74
 
0.2%
Leicester City74
 
0.2%
Cardiff City73
 
0.2%
Real Valladolid CF73
 
0.2%
Other values (810)36796
98.0%

Length

2022-01-13T14:14:05.155288image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fc5824
 
7.3%
de1489
 
1.9%
club1251
 
1.6%
united1168
 
1.5%
city1152
 
1.4%
atlético829
 
1.0%
al799
 
1.0%
cd757
 
0.9%
sc663
 
0.8%
town658
 
0.8%
Other values (1157)65668
81.8%

Most occurring characters

ValueCountFrequency (%)
42776
 
8.6%
a39217
 
7.9%
e38942
 
7.8%
n29540
 
5.9%
r27544
 
5.5%
o27382
 
5.5%
i26845
 
5.4%
l22370
 
4.5%
t21988
 
4.4%
s17716
 
3.6%
Other values (95)202153
40.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter350730
70.6%
Uppercase Letter97378
 
19.6%
Space Separator42776
 
8.6%
Decimal Number3290
 
0.7%
Other Punctuation1617
 
0.3%
Dash Punctuation614
 
0.1%
Open Punctuation34
 
< 0.1%
Close Punctuation34
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a39217
11.2%
e38942
11.1%
n29540
 
8.4%
r27544
 
7.9%
o27382
 
7.8%
i26845
 
7.7%
l22370
 
6.4%
t21988
 
6.3%
s17716
 
5.1%
d13089
 
3.7%
Other values (46)86097
24.5%
Uppercase Letter
ValueCountFrequency (%)
C16219
16.7%
F10286
 
10.6%
S10071
 
10.3%
A7383
 
7.6%
B4848
 
5.0%
K4032
 
4.1%
M3672
 
3.8%
R3670
 
3.8%
P3593
 
3.7%
L3544
 
3.6%
Other values (21)30060
30.9%
Decimal Number
ValueCountFrequency (%)
1867
26.4%
9531
16.1%
8524
15.9%
0465
14.1%
4284
 
8.6%
6215
 
6.5%
3140
 
4.3%
5112
 
3.4%
282
 
2.5%
770
 
2.1%
Other Punctuation
ValueCountFrequency (%)
.1296
80.1%
'171
 
10.6%
&115
 
7.1%
/35
 
2.2%
Space Separator
ValueCountFrequency (%)
42776
100.0%
Dash Punctuation
ValueCountFrequency (%)
-614
100.0%
Open Punctuation
ValueCountFrequency (%)
(34
100.0%
Close Punctuation
ValueCountFrequency (%)
)34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin448108
90.3%
Common48365
 
9.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a39217
 
8.8%
e38942
 
8.7%
n29540
 
6.6%
r27544
 
6.1%
o27382
 
6.1%
i26845
 
6.0%
l22370
 
5.0%
t21988
 
4.9%
s17716
 
4.0%
C16219
 
3.6%
Other values (77)180345
40.2%
Common
ValueCountFrequency (%)
42776
88.4%
.1296
 
2.7%
1867
 
1.8%
-614
 
1.3%
9531
 
1.1%
8524
 
1.1%
0465
 
1.0%
4284
 
0.6%
6215
 
0.4%
'171
 
0.4%
Other values (8)622
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII489065
98.5%
Latin 1 Sup6277
 
1.3%
Latin Ext A1131
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
42776
 
8.7%
a39217
 
8.0%
e38942
 
8.0%
n29540
 
6.0%
r27544
 
5.6%
o27382
 
5.6%
i26845
 
5.5%
l22370
 
4.6%
t21988
 
4.5%
s17716
 
3.6%
Other values (60)194745
39.8%
Latin 1 Sup
ValueCountFrequency (%)
é1324
21.1%
ó1027
16.4%
ü655
10.4%
ö536
8.5%
á431
 
6.9%
ø400
 
6.4%
í304
 
4.8%
ú199
 
3.2%
ñ173
 
2.8%
ç139
 
2.2%
Other values (15)1089
17.3%
Latin Ext A
ValueCountFrequency (%)
ş440
38.9%
ł302
26.7%
ń170
 
15.0%
ę87
 
7.7%
ğ37
 
3.3%
ň34
 
3.0%
ą21
 
1.9%
ı19
 
1.7%
ź19
 
1.7%
Ś2
 
0.2%

overall
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct49
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.29082774
Minimum46
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size293.5 KiB
2022-01-13T14:14:05.226772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile54
Q162
median66
Q371
95-th percentile78
Maximum94
Range48
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.993904337
Coefficient of variation (CV)0.105503349
Kurtosis0.03719615588
Mean66.29082774
Median Absolute Deviation (MAD)5
Skewness0.04304902729
Sum2489088
Variance48.91469787
MonotonicityNot monotonic
2022-01-13T14:14:05.286320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
662323
 
6.2%
642246
 
6.0%
672238
 
6.0%
652221
 
5.9%
682161
 
5.8%
691977
 
5.3%
631948
 
5.2%
701827
 
4.9%
621738
 
4.6%
711568
 
4.2%
Other values (39)17301
46.1%
ValueCountFrequency (%)
466
 
< 0.1%
4728
 
0.1%
4867
 
0.2%
49116
 
0.3%
50216
 
0.6%
51262
0.7%
52353
0.9%
53443
1.2%
54480
1.3%
55596
1.6%
ValueCountFrequency (%)
943
 
< 0.1%
932
 
< 0.1%
924
 
< 0.1%
918
 
< 0.1%
9012
 
< 0.1%
8925
0.1%
8834
0.1%
8730
0.1%
8640
0.1%
8555
0.1%

potential
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct49
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.43038244
Minimum46
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size293.5 KiB
2022-01-13T14:14:05.338445image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile62
Q167
median71
Q375
95-th percentile82
Maximum95
Range49
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.128342593
Coefficient of variation (CV)0.08579462105
Kurtosis0.07009830682
Mean71.43038244
Median Absolute Deviation (MAD)4
Skewness0.2271802164
Sum2682068
Variance37.55658294
MonotonicityNot monotonic
2022-01-13T14:14:05.416662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
702478
 
6.6%
712384
 
6.3%
722371
 
6.3%
682359
 
6.3%
692351
 
6.3%
732232
 
5.9%
672131
 
5.7%
742109
 
5.6%
751964
 
5.2%
661880
 
5.0%
Other values (39)15289
40.7%
ValueCountFrequency (%)
462
 
< 0.1%
483
 
< 0.1%
492
 
< 0.1%
504
 
< 0.1%
517
 
< 0.1%
5216
 
< 0.1%
5313
 
< 0.1%
5425
 
0.1%
5544
0.1%
5665
0.2%
ValueCountFrequency (%)
952
 
< 0.1%
947
 
< 0.1%
9312
 
< 0.1%
9221
 
0.1%
9122
 
0.1%
9035
 
0.1%
8959
 
0.2%
8883
 
0.2%
87140
0.4%
86212
0.6%

value
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct243
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2467932.646
Minimum0
Maximum123000000
Zeros523
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size293.5 KiB
2022-01-13T14:14:05.508929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile90000
Q1325000
median700000
Q32100000
95-th percentile10500000
Maximum123000000
Range123000000
Interquartile range (IQR)1775000

Descriptive statistics

Standard deviation5646339.227
Coefficient of variation (CV)2.287882222
Kurtosis77.72967875
Mean2467932.646
Median Absolute Deviation (MAD)500000
Skewness7.05609803
Sum9.2665935 × 1010
Variance3.188114667 × 1013
MonotonicityNot monotonic
2022-01-13T14:14:05.610112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1100000908
 
2.4%
325000729
 
1.9%
1200000709
 
1.9%
375000700
 
1.9%
425000686
 
1.8%
525000681
 
1.8%
450000672
 
1.8%
350000669
 
1.8%
1000000651
 
1.7%
400000624
 
1.7%
Other values (233)30519
81.3%
ValueCountFrequency (%)
0523
1.4%
1000026
 
0.1%
2000030
 
0.1%
3000063
 
0.2%
40000130
 
0.3%
50000256
0.7%
60000323
0.9%
70000249
0.7%
80000262
0.7%
90000306
0.8%
ValueCountFrequency (%)
1230000001
< 0.1%
1185000001
< 0.1%
1105000001
< 0.1%
1055000001
< 0.1%
1050000001
< 0.1%
1020000001
< 0.1%
970000001
< 0.1%
955000001
< 0.1%
935000001
< 0.1%
930000001
< 0.1%

player_positions
Categorical

HIGH CARDINALITY

Distinct1083
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
CB
4671 
GK
4192 
ST
3795 
CM
 
1542
CDM, CM
 
1456
Other values (1078)
21892 

Length

Max length17
Median length3
Mean length4.791866411
Min length2

Characters and Unicode

Total characters179925
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique498 ?
Unique (%)1.3%

Sample

1st rowCM, CDM
2nd rowCM, CDM, CAM
3rd rowST, CAM
4th rowST
5th rowCDM

Common Values

ValueCountFrequency (%)
CB4671
 
12.4%
GK4192
 
11.2%
ST3795
 
10.1%
CM1542
 
4.1%
CDM, CM1456
 
3.9%
LB1428
 
3.8%
CM, CDM1286
 
3.4%
RB1211
 
3.2%
CDM771
 
2.1%
CAM646
 
1.7%
Other values (1073)16550
44.1%

Length

2022-01-13T14:14:05.764112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cm7673
12.6%
cb7658
12.6%
st6819
11.2%
cdm5616
9.2%
lm5202
8.5%
rm5092
8.4%
cam4619
7.6%
lb4266
7.0%
gk4192
6.9%
rb4168
6.8%
Other values (5)5622
9.2%

Most occurring characters

ValueCountFrequency (%)
M28202
15.7%
C26335
14.6%
,23379
13.0%
23379
13.0%
B17170
9.5%
L11909
6.6%
R11672
6.5%
S6819
 
3.8%
T6819
 
3.8%
D5616
 
3.1%
Other values (5)18625
10.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter133167
74.0%
Other Punctuation23379
 
13.0%
Space Separator23379
 
13.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M28202
21.2%
C26335
19.8%
B17170
12.9%
L11909
8.9%
R11672
8.8%
S6819
 
5.1%
T6819
 
5.1%
D5616
 
4.2%
W4853
 
3.6%
A4619
 
3.5%
Other values (3)9153
 
6.9%
Other Punctuation
ValueCountFrequency (%)
,23379
100.0%
Space Separator
ValueCountFrequency (%)
23379
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin133167
74.0%
Common46758
 
26.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M28202
21.2%
C26335
19.8%
B17170
12.9%
L11909
8.9%
R11672
8.8%
S6819
 
5.1%
T6819
 
5.1%
D5616
 
4.2%
W4853
 
3.6%
A4619
 
3.5%
Other values (3)9153
 
6.9%
Common
ValueCountFrequency (%)
,23379
50.0%
23379
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII179925
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M28202
15.7%
C26335
14.6%
,23379
13.0%
23379
13.0%
B17170
9.5%
L11909
6.6%
R11672
6.5%
S6819
 
3.8%
T6819
 
3.8%
D5616
 
3.1%
Other values (5)18625
10.4%

preferred_foot
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
Right
28674 
Left
8874 

Length

Max length5
Median length5
Mean length4.763662512
Min length4

Characters and Unicode

Total characters178866
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRight
2nd rowRight
3rd rowRight
4th rowRight
5th rowRight

Common Values

ValueCountFrequency (%)
Right28674
76.4%
Left8874
 
23.6%

Length

2022-01-13T14:14:05.930554image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-13T14:14:05.974449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
right28674
76.4%
left8874
 
23.6%

Most occurring characters

ValueCountFrequency (%)
t37548
21.0%
R28674
16.0%
i28674
16.0%
g28674
16.0%
h28674
16.0%
L8874
 
5.0%
e8874
 
5.0%
f8874
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter141318
79.0%
Uppercase Letter37548
 
21.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t37548
26.6%
i28674
20.3%
g28674
20.3%
h28674
20.3%
e8874
 
6.3%
f8874
 
6.3%
Uppercase Letter
ValueCountFrequency (%)
R28674
76.4%
L8874
 
23.6%

Most occurring scripts

ValueCountFrequency (%)
Latin178866
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t37548
21.0%
R28674
16.0%
i28674
16.0%
g28674
16.0%
h28674
16.0%
L8874
 
5.0%
e8874
 
5.0%
f8874
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII178866
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t37548
21.0%
R28674
16.0%
i28674
16.0%
g28674
16.0%
h28674
16.0%
L8874
 
5.0%
e8874
 
5.0%
f8874
 
5.0%

international_reputation
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
1
34041 
2
 
2739
3
 
649
4
 
104
5
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37548
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
134041
90.7%
22739
 
7.3%
3649
 
1.7%
4104
 
0.3%
515
 
< 0.1%

Length

2022-01-13T14:14:06.380740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-13T14:14:06.440604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
134041
90.7%
22739
 
7.3%
3649
 
1.7%
4104
 
0.3%
515
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
134041
90.7%
22739
 
7.3%
3649
 
1.7%
4104
 
0.3%
515
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number37548
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
134041
90.7%
22739
 
7.3%
3649
 
1.7%
4104
 
0.3%
515
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common37548
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
134041
90.7%
22739
 
7.3%
3649
 
1.7%
4104
 
0.3%
515
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII37548
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
134041
90.7%
22739
 
7.3%
3649
 
1.7%
4104
 
0.3%
515
 
< 0.1%

weak_foot
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
3
23337 
2
7873 
4
5535 
5
 
488
1
 
315

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37548
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
323337
62.2%
27873
 
21.0%
45535
 
14.7%
5488
 
1.3%
1315
 
0.8%

Length

2022-01-13T14:14:06.539322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-13T14:14:06.575393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
323337
62.2%
27873
 
21.0%
45535
 
14.7%
5488
 
1.3%
1315
 
0.8%

Most occurring characters

ValueCountFrequency (%)
323337
62.2%
27873
 
21.0%
45535
 
14.7%
5488
 
1.3%
1315
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number37548
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
323337
62.2%
27873
 
21.0%
45535
 
14.7%
5488
 
1.3%
1315
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common37548
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
323337
62.2%
27873
 
21.0%
45535
 
14.7%
5488
 
1.3%
1315
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII37548
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
323337
62.2%
27873
 
21.0%
45535
 
14.7%
5488
 
1.3%
1315
 
0.8%

skill_moves
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
2
18273 
3
13048 
1
4192 
4
1931 
5
 
104

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37548
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row3
3rd row4
4th row2
5th row2

Common Values

ValueCountFrequency (%)
218273
48.7%
313048
34.8%
14192
 
11.2%
41931
 
5.1%
5104
 
0.3%

Length

2022-01-13T14:14:06.727259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-13T14:14:06.762328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
218273
48.7%
313048
34.8%
14192
 
11.2%
41931
 
5.1%
5104
 
0.3%

Most occurring characters

ValueCountFrequency (%)
218273
48.7%
313048
34.8%
14192
 
11.2%
41931
 
5.1%
5104
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number37548
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
218273
48.7%
313048
34.8%
14192
 
11.2%
41931
 
5.1%
5104
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common37548
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
218273
48.7%
313048
34.8%
14192
 
11.2%
41931
 
5.1%
5104
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII37548
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
218273
48.7%
313048
34.8%
14192
 
11.2%
41931
 
5.1%
5104
 
0.3%

work_rate
Categorical

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
Medium/Medium
20433 
High/Medium
6629 
Medium/High
3424 
High/High
 
1956
Medium/Low
 
1759
Other values (4)
3347 

Length

Max length13
Median length13
Mean length11.71761479
Min length7

Characters and Unicode

Total characters439973
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedium/Medium
2nd rowMedium/Medium
3rd rowHigh/Medium
4th rowHigh/Low
5th rowMedium/Medium

Common Values

ValueCountFrequency (%)
Medium/Medium20433
54.4%
High/Medium6629
 
17.7%
Medium/High3424
 
9.1%
High/High1956
 
5.2%
Medium/Low1759
 
4.7%
High/Low1463
 
3.9%
Low/Medium927
 
2.5%
Low/High894
 
2.4%
Low/Low63
 
0.2%

Length

2022-01-13T14:14:06.861595image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-13T14:14:06.910521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
medium/medium20433
54.4%
high/medium6629
 
17.7%
medium/high3424
 
9.1%
high/high1956
 
5.2%
medium/low1759
 
4.7%
high/low1463
 
3.9%
low/medium927
 
2.5%
low/high894
 
2.4%
low/low63
 
0.2%

Most occurring characters

ValueCountFrequency (%)
i69927
15.9%
M53605
12.2%
e53605
12.2%
d53605
12.2%
u53605
12.2%
m53605
12.2%
/37548
8.5%
H16322
 
3.7%
g16322
 
3.7%
h16322
 
3.7%
Other values (3)15507
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter327329
74.4%
Uppercase Letter75096
 
17.1%
Other Punctuation37548
 
8.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i69927
21.4%
e53605
16.4%
d53605
16.4%
u53605
16.4%
m53605
16.4%
g16322
 
5.0%
h16322
 
5.0%
o5169
 
1.6%
w5169
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
M53605
71.4%
H16322
 
21.7%
L5169
 
6.9%
Other Punctuation
ValueCountFrequency (%)
/37548
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin402425
91.5%
Common37548
 
8.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
i69927
17.4%
M53605
13.3%
e53605
13.3%
d53605
13.3%
u53605
13.3%
m53605
13.3%
H16322
 
4.1%
g16322
 
4.1%
h16322
 
4.1%
L5169
 
1.3%
Other values (2)10338
 
2.6%
Common
ValueCountFrequency (%)
/37548
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII439973
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i69927
15.9%
M53605
12.2%
e53605
12.2%
d53605
12.2%
u53605
12.2%
m53605
12.2%
/37548
8.5%
H16322
 
3.7%
g16322
 
3.7%
h16322
 
3.7%
Other values (3)15507
 
3.5%

body_type
Categorical

HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
Normal
22016 
Lean
13239 
Stocky
2276 
Neymar
 
3
C. Ronaldo
 
3
Other values (5)
 
11

Length

Max length19
Median length6
Mean length5.296127623
Min length4

Characters and Unicode

Total characters198859
Distinct characters38
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal
2nd rowLean
3rd rowNormal
4th rowNormal
5th rowLean

Common Values

ValueCountFrequency (%)
Normal22016
58.6%
Lean13239
35.3%
Stocky2276
 
6.1%
Neymar3
 
< 0.1%
C. Ronaldo3
 
< 0.1%
Shaqiri3
 
< 0.1%
Messi2
 
< 0.1%
Courtois2
 
< 0.1%
Akinfenwa2
 
< 0.1%
PLAYER_BODY_TYPE_252
 
< 0.1%

Length

2022-01-13T14:14:07.015068image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-13T14:14:07.053974image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
normal22016
58.6%
lean13239
35.3%
stocky2276
 
6.1%
shaqiri3
 
< 0.1%
c3
 
< 0.1%
neymar3
 
< 0.1%
ronaldo3
 
< 0.1%
messi2
 
< 0.1%
akinfenwa2
 
< 0.1%
courtois2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
a35266
17.7%
o24302
12.2%
r22024
11.1%
N22019
11.1%
m22019
11.1%
l22019
11.1%
e13246
 
6.7%
n13246
 
6.7%
L13241
 
6.7%
S2279
 
1.1%
Other values (28)9198
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter161266
81.1%
Uppercase Letter37577
 
18.9%
Connector Punctuation6
 
< 0.1%
Decimal Number4
 
< 0.1%
Other Punctuation3
 
< 0.1%
Space Separator3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a35266
21.9%
o24302
15.1%
r22024
13.7%
m22019
13.7%
l22019
13.7%
e13246
 
8.2%
n13246
 
8.2%
y2279
 
1.4%
t2278
 
1.4%
k2278
 
1.4%
Other values (9)2309
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
N22019
58.6%
L13241
35.2%
S2279
 
6.1%
Y6
 
< 0.1%
C5
 
< 0.1%
R5
 
< 0.1%
A4
 
< 0.1%
P4
 
< 0.1%
E4
 
< 0.1%
B2
 
< 0.1%
Other values (4)8
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
22
50.0%
52
50.0%
Connector Punctuation
ValueCountFrequency (%)
_6
100.0%
Other Punctuation
ValueCountFrequency (%)
.3
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin198843
> 99.9%
Common16
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a35266
17.7%
o24302
12.2%
r22024
11.1%
N22019
11.1%
m22019
11.1%
l22019
11.1%
e13246
 
6.7%
n13246
 
6.7%
L13241
 
6.7%
S2279
 
1.1%
Other values (23)9182
 
4.6%
Common
ValueCountFrequency (%)
_6
37.5%
.3
18.8%
3
18.8%
22
 
12.5%
52
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII198859
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a35266
17.7%
o24302
12.2%
r22024
11.1%
N22019
11.1%
m22019
11.1%
l22019
11.1%
e13246
 
6.7%
n13246
 
6.7%
L13241
 
6.7%
S2279
 
1.1%
Other values (28)9198
 
4.6%

real_face
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.8 KiB
False
34209 
True
 
3339
ValueCountFrequency (%)
False34209
91.1%
True3339
 
8.9%
2022-01-13T14:14:07.092984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

release_clause_eur
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1438
Distinct (%)4.2%
Missing3020
Missing (%)8.0%
Infinite0
Infinite (%)0.0%
Mean4695537.1
Minimum13000
Maximum236800000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size293.5 KiB
2022-01-13T14:14:07.132250image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum13000
5-th percentile171000
Q1538000
median1200000
Q33700000
95-th percentile20000000
Maximum236800000
Range236787000
Interquartile range (IQR)3162000

Descriptive statistics

Standard deviation11204861.6
Coefficient of variation (CV)2.386279006
Kurtosis77.73687183
Mean4695537.1
Median Absolute Deviation (MAD)849000
Skewness7.083673445
Sum1.62127505 × 1011
Variance1.255489235 × 1014
MonotonicityNot monotonic
2022-01-13T14:14:07.192185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11000001131
 
3.0%
1200000846
 
2.3%
1300000846
 
2.3%
1400000775
 
2.1%
1500000686
 
1.8%
1600000662
 
1.8%
1000000609
 
1.6%
1700000560
 
1.5%
1800000463
 
1.2%
1900000432
 
1.2%
Other values (1428)27518
73.3%
(Missing)3020
 
8.0%
ValueCountFrequency (%)
1300011
< 0.1%
160001
 
< 0.1%
170001
 
< 0.1%
1800012
< 0.1%
220001
 
< 0.1%
250009
< 0.1%
260001
 
< 0.1%
270002
 
< 0.1%
290002
 
< 0.1%
310003
 
< 0.1%
ValueCountFrequency (%)
2368000001
< 0.1%
2281000001
< 0.1%
2265000001
< 0.1%
2153000001
< 0.1%
1989000001
< 0.1%
1964000001
< 0.1%
1958000001
< 0.1%
1952000001
< 0.1%
1917000001
< 0.1%
1845000001
< 0.1%

player_tags
Categorical

HIGH CARDINALITY
MISSING

Distinct109
Distinct (%)3.5%
Missing34410
Missing (%)91.6%
Memory size1.3 MiB
#Strength
1030 
#Acrobat
502 
#Engine
436 
#Speedster
336 
#Speedster, #Acrobat
131 
Other values (104)
703 

Length

Max length132
Median length9
Mean length12.19088591
Min length7

Characters and Unicode

Total characters38255
Distinct characters34
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)1.6%

Sample

1st row#Distance Shooter
2nd row#Acrobat
3rd row#Dribbler
4th row#Strength
5th row#Speedster, #Acrobat

Common Values

ValueCountFrequency (%)
#Strength1030
 
2.7%
#Acrobat502
 
1.3%
#Engine436
 
1.2%
#Speedster336
 
0.9%
#Speedster, #Acrobat131
 
0.3%
#Aerial Threat, #Strength116
 
0.3%
#Aerial Threat74
 
0.2%
#Dribbler71
 
0.2%
#Dribbler, #Acrobat38
 
0.1%
#Speedster, #Dribbler, #Acrobat36
 
0.1%
Other values (99)368
 
1.0%
(Missing)34410
91.6%

Length

2022-01-13T14:14:07.329850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
strength1189
27.1%
acrobat769
17.5%
speedster535
12.2%
engine494
11.2%
dribbler217
 
4.9%
aerial207
 
4.7%
threat207
 
4.7%
93
 
2.1%
tactician74
 
1.7%
tackling68
 
1.5%
Other values (13)539
12.3%

Most occurring characters

ValueCountFrequency (%)
e4443
 
11.6%
t4148
 
10.8%
#3895
 
10.2%
r3716
 
9.7%
n2476
 
6.5%
S1812
 
4.7%
g1751
 
4.6%
a1704
 
4.5%
i1524
 
4.0%
h1514
 
4.0%
Other values (24)11272
29.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter27835
72.8%
Other Punctuation4652
 
12.2%
Uppercase Letter4347
 
11.4%
Space Separator1421
 
3.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e4443
16.0%
t4148
14.9%
r3716
13.4%
n2476
8.9%
g1751
 
6.3%
a1704
 
6.1%
i1524
 
5.5%
h1514
 
5.4%
b1203
 
4.3%
c1151
 
4.1%
Other values (10)4205
15.1%
Uppercase Letter
ValueCountFrequency (%)
S1812
41.7%
A976
22.5%
E494
 
11.4%
T349
 
8.0%
D270
 
6.2%
C169
 
3.9%
F121
 
2.8%
P85
 
2.0%
K48
 
1.1%
M23
 
0.5%
Other Punctuation
ValueCountFrequency (%)
#3895
83.7%
,757
 
16.3%
Space Separator
ValueCountFrequency (%)
1220
85.9%
 201
 
14.1%

Most occurring scripts

ValueCountFrequency (%)
Latin32182
84.1%
Common6073
 
15.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e4443
13.8%
t4148
12.9%
r3716
11.5%
n2476
 
7.7%
S1812
 
5.6%
g1751
 
5.4%
a1704
 
5.3%
i1524
 
4.7%
h1514
 
4.7%
b1203
 
3.7%
Other values (20)7891
24.5%
Common
ValueCountFrequency (%)
#3895
64.1%
1220
 
20.1%
,757
 
12.5%
 201
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII38054
99.5%
Latin 1 Sup201
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e4443
11.7%
t4148
 
10.9%
#3895
 
10.2%
r3716
 
9.8%
n2476
 
6.5%
S1812
 
4.8%
g1751
 
4.6%
a1704
 
4.5%
i1524
 
4.0%
h1514
 
4.0%
Other values (23)11071
29.1%
Latin 1 Sup
ValueCountFrequency (%)
 201
100.0%

team_position
Categorical

HIGH CORRELATION
MISSING

Distinct29
Distinct (%)0.1%
Missing502
Missing (%)1.3%
Memory size2.1 MiB
SUB
16007 
RES
6163 
GK
 
1353
RCB
 
1349
LCB
 
1326
Other values (24)
10848 

Length

Max length3
Median length3
Mean length2.777654808
Min length2

Characters and Unicode

Total characters102901
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRDM
2nd rowLDM
3rd rowSUB
4th rowST
5th rowLCB

Common Values

ValueCountFrequency (%)
SUB16007
42.6%
RES6163
 
16.4%
GK1353
 
3.6%
RCB1349
 
3.6%
LCB1326
 
3.5%
LB1176
 
3.1%
RB1139
 
3.0%
ST919
 
2.4%
RM882
 
2.3%
LM881
 
2.3%
Other values (19)5851
 
15.6%

Length

2022-01-13T14:14:08.679846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sub16007
43.2%
res6163
 
16.6%
gk1353
 
3.7%
rcb1349
 
3.6%
lcb1326
 
3.6%
lb1176
 
3.2%
rb1139
 
3.1%
st919
 
2.5%
rm882
 
2.4%
lm881
 
2.4%
Other values (19)5851
 
15.8%

Most occurring characters

ValueCountFrequency (%)
S23929
23.3%
B21405
20.8%
U16007
15.6%
R11787
11.5%
E6163
 
6.0%
M5668
 
5.5%
C5651
 
5.5%
L5631
 
5.5%
D1369
 
1.3%
G1353
 
1.3%
Other values (5)3938
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter102901
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S23929
23.3%
B21405
20.8%
U16007
15.6%
R11787
11.5%
E6163
 
6.0%
M5668
 
5.5%
C5651
 
5.5%
L5631
 
5.5%
D1369
 
1.3%
G1353
 
1.3%
Other values (5)3938
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Latin102901
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S23929
23.3%
B21405
20.8%
U16007
15.6%
R11787
11.5%
E6163
 
6.0%
M5668
 
5.5%
C5651
 
5.5%
L5631
 
5.5%
D1369
 
1.3%
G1353
 
1.3%
Other values (5)3938
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII102901
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S23929
23.3%
B21405
20.8%
U16007
15.6%
R11787
11.5%
E6163
 
6.0%
M5668
 
5.5%
C5651
 
5.5%
L5631
 
5.5%
D1369
 
1.3%
G1353
 
1.3%
Other values (5)3938
 
3.8%

team_jersey_number
Real number (ℝ≥0)

MISSING

Distinct99
Distinct (%)0.3%
Missing502
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean19.92336555
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size293.5 KiB
2022-01-13T14:14:08.740421image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median17
Q327
95-th percentile45
Maximum99
Range98
Interquartile range (IQR)19

Descriptive statistics

Standard deviation16.39872615
Coefficient of variation (CV)0.8230901607
Kurtosis6.637976948
Mean19.92336555
Median Absolute Deviation (MAD)9
Skewness2.143918214
Sum738081
Variance268.9182193
MonotonicityNot monotonic
2022-01-13T14:14:08.795291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101260
 
3.4%
71255
 
3.3%
81228
 
3.3%
11197
 
3.2%
51194
 
3.2%
111163
 
3.1%
201161
 
3.1%
91155
 
3.1%
61151
 
3.1%
41140
 
3.0%
Other values (89)25142
67.0%
ValueCountFrequency (%)
11197
3.2%
21026
2.7%
31075
2.9%
41140
3.0%
51194
3.2%
61151
3.1%
71255
3.3%
81228
3.3%
91155
3.1%
101260
3.4%
ValueCountFrequency (%)
99143
0.4%
9837
 
0.1%
9739
 
0.1%
9629
 
0.1%
9538
 
0.1%
9425
 
0.1%
9341
 
0.1%
9222
 
0.1%
9142
 
0.1%
9050
 
0.1%

loaned_from
Categorical

HIGH CARDINALITY
MISSING

Distinct435
Distinct (%)17.4%
Missing35051
Missing (%)93.3%
Memory size1.2 MiB
Atalanta
 
39
Sassuolo
 
34
Manchester City
 
29
Inter
 
29
SL Benfica
 
27
Other values (430)
2339 

Length

Max length35
Median length11
Mean length12.29395274
Min length3

Characters and Unicode

Total characters30698
Distinct characters92
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique116 ?
Unique (%)4.6%

Sample

1st rowRoyal Excel Mouscron
2nd rowSheffield United
3rd rowAmiens SC
4th rowNapoli
5th rowManchester City

Common Values

ValueCountFrequency (%)
Atalanta39
 
0.1%
Sassuolo34
 
0.1%
Manchester City29
 
0.1%
Inter29
 
0.1%
SL Benfica27
 
0.1%
Tigres U.A.N.L.26
 
0.1%
Genoa26
 
0.1%
Sporting CP25
 
0.1%
Chelsea25
 
0.1%
FC Porto25
 
0.1%
Other values (425)2212
 
5.9%
(Missing)35051
93.3%

Length

2022-01-13T14:14:08.914632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fc264
 
5.4%
city122
 
2.5%
club112
 
2.3%
united110
 
2.3%
de91
 
1.9%
atlético68
 
1.4%
cf65
 
1.3%
manchester45
 
0.9%
real45
 
0.9%
al44
 
0.9%
Other values (644)3898
80.1%

Most occurring characters

ValueCountFrequency (%)
e2484
 
8.1%
a2455
 
8.0%
2367
 
7.7%
n1877
 
6.1%
i1791
 
5.8%
o1754
 
5.7%
r1712
 
5.6%
l1604
 
5.2%
t1602
 
5.2%
s1017
 
3.3%
Other values (82)12035
39.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter22198
72.3%
Uppercase Letter5733
 
18.7%
Space Separator2367
 
7.7%
Other Punctuation192
 
0.6%
Decimal Number171
 
0.6%
Dash Punctuation31
 
0.1%
Open Punctuation3
 
< 0.1%
Close Punctuation3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e2484
11.2%
a2455
11.1%
n1877
 
8.5%
i1791
 
8.1%
o1754
 
7.9%
r1712
 
7.7%
l1604
 
7.2%
t1602
 
7.2%
s1017
 
4.6%
d918
 
4.1%
Other values (37)4984
22.5%
Uppercase Letter
ValueCountFrequency (%)
C963
16.8%
S604
 
10.5%
A479
 
8.4%
F477
 
8.3%
B374
 
6.5%
L296
 
5.2%
M277
 
4.8%
R275
 
4.8%
U220
 
3.8%
P188
 
3.3%
Other values (19)1580
27.6%
Decimal Number
ValueCountFrequency (%)
144
25.7%
939
22.8%
028
16.4%
821
12.3%
417
 
9.9%
58
 
4.7%
67
 
4.1%
34
 
2.3%
73
 
1.8%
Other Punctuation
ValueCountFrequency (%)
.157
81.8%
&26
 
13.5%
'9
 
4.7%
Space Separator
ValueCountFrequency (%)
2367
100.0%
Dash Punctuation
ValueCountFrequency (%)
-31
100.0%
Open Punctuation
ValueCountFrequency (%)
(3
100.0%
Close Punctuation
ValueCountFrequency (%)
)3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin27931
91.0%
Common2767
 
9.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e2484
 
8.9%
a2455
 
8.8%
n1877
 
6.7%
i1791
 
6.4%
o1754
 
6.3%
r1712
 
6.1%
l1604
 
5.7%
t1602
 
5.7%
s1017
 
3.6%
C963
 
3.4%
Other values (66)10672
38.2%
Common
ValueCountFrequency (%)
2367
85.5%
.157
 
5.7%
144
 
1.6%
939
 
1.4%
-31
 
1.1%
028
 
1.0%
&26
 
0.9%
821
 
0.8%
417
 
0.6%
'9
 
0.3%
Other values (6)28
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII30386
99.0%
Latin 1 Sup288
 
0.9%
Latin Ext A24
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e2484
 
8.2%
a2455
 
8.1%
2367
 
7.8%
n1877
 
6.2%
i1791
 
5.9%
o1754
 
5.8%
r1712
 
5.6%
l1604
 
5.3%
t1602
 
5.3%
s1017
 
3.3%
Other values (58)11723
38.6%
Latin 1 Sup
ValueCountFrequency (%)
é114
39.6%
ó39
 
13.5%
á24
 
8.3%
ü20
 
6.9%
ö16
 
5.6%
í14
 
4.9%
É10
 
3.5%
è9
 
3.1%
ñ8
 
2.8%
ú7
 
2.4%
Other values (10)27
 
9.4%
Latin Ext A
ValueCountFrequency (%)
ş15
62.5%
ń6
 
25.0%
ł2
 
8.3%
ę1
 
4.2%

joined
Categorical

HIGH CARDINALITY
MISSING

Distinct2189
Distinct (%)6.3%
Missing2999
Missing (%)8.0%
Memory size2.3 MiB
2017-07-01
 
2290
2018-07-01
 
1951
2016-07-01
 
1389
2019-07-01
 
1022
2015-07-01
 
860
Other values (2184)
27037 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters345490
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique444 ?
Unique (%)1.3%

Sample

1st row2017-07-01
2nd row2018-01-29
3rd row2015-08-29
4th row2019-07-18
5th row2017-01-07

Common Values

ValueCountFrequency (%)
2017-07-012290
 
6.1%
2018-07-011951
 
5.2%
2016-07-011389
 
3.7%
2019-07-011022
 
2.7%
2015-07-01860
 
2.3%
2018-01-01625
 
1.7%
2014-07-01516
 
1.4%
2017-01-01495
 
1.3%
2016-01-01456
 
1.2%
2015-01-01396
 
1.1%
Other values (2179)24549
65.4%
(Missing)2999
 
8.0%

Length

2022-01-13T14:14:09.019491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-07-012290
 
6.6%
2018-07-011951
 
5.6%
2016-07-011389
 
4.0%
2019-07-011022
 
3.0%
2015-07-01860
 
2.5%
2018-01-01625
 
1.8%
2014-07-01516
 
1.5%
2017-01-01495
 
1.4%
2016-01-01456
 
1.3%
2015-01-01396
 
1.1%
Other values (2179)24549
71.1%

Most occurring characters

ValueCountFrequency (%)
091649
26.5%
-69098
20.0%
166342
19.2%
245244
13.1%
727073
 
7.8%
813925
 
4.0%
69291
 
2.7%
96681
 
1.9%
55972
 
1.7%
35848
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number276392
80.0%
Dash Punctuation69098
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
091649
33.2%
166342
24.0%
245244
16.4%
727073
 
9.8%
813925
 
5.0%
69291
 
3.4%
96681
 
2.4%
55972
 
2.2%
35848
 
2.1%
44367
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
-69098
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common345490
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
091649
26.5%
-69098
20.0%
166342
19.2%
245244
13.1%
727073
 
7.8%
813925
 
4.0%
69291
 
2.7%
96681
 
1.9%
55972
 
1.7%
35848
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII345490
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
091649
26.5%
-69098
20.0%
166342
19.2%
245244
13.1%
727073
 
7.8%
813925
 
4.0%
69291
 
2.7%
96681
 
1.9%
55972
 
1.7%
35848
 
1.7%

contract_valid_until
Real number (ℝ≥0)

MISSING

Distinct10
Distinct (%)< 0.1%
Missing502
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean2020.155428
Minimum2017
Maximum2026
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size293.5 KiB
2022-01-13T14:14:09.056686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2018
Q12019
median2020
Q32021
95-th percentile2023
Maximum2026
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.531467241
Coefficient of variation (CV)0.0007580937683
Kurtosis-0.350227421
Mean2020.155428
Median Absolute Deviation (MAD)1
Skewness0.2768468249
Sum74838678
Variance2.345391911
MonotonicityNot monotonic
2022-01-13T14:14:09.091526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
20209941
26.5%
20197426
19.8%
20216855
18.3%
20224708
12.5%
20184703
12.5%
20231944
 
5.2%
2017774
 
2.1%
2024672
 
1.8%
202517
 
< 0.1%
20266
 
< 0.1%
(Missing)502
 
1.3%
ValueCountFrequency (%)
2017774
 
2.1%
20184703
12.5%
20197426
19.8%
20209941
26.5%
20216855
18.3%
20224708
12.5%
20231944
 
5.2%
2024672
 
1.8%
202517
 
< 0.1%
20266
 
< 0.1%
ValueCountFrequency (%)
20266
 
< 0.1%
202517
 
< 0.1%
2024672
 
1.8%
20231944
 
5.2%
20224708
12.5%
20216855
18.3%
20209941
26.5%
20197426
19.8%
20184703
12.5%
2017774
 
2.1%

nation_position
Categorical

HIGH CORRELATION
MISSING

Distinct28
Distinct (%)1.2%
Missing35147
Missing (%)93.6%
Memory size1.2 MiB
SUB
1236 
RCB
 
113
LCB
 
101
GK
 
98
LB
 
90
Other values (23)
763 

Length

Max length3
Median length3
Mean length2.728029988
Min length2

Characters and Unicode

Total characters6550
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSUB
2nd rowLDM
3rd rowLM
4th rowSUB
5th rowRB

Common Values

ValueCountFrequency (%)
SUB1236
 
3.3%
RCB113
 
0.3%
LCB101
 
0.3%
GK98
 
0.3%
LB90
 
0.2%
RB88
 
0.2%
LM77
 
0.2%
ST77
 
0.2%
RM72
 
0.2%
LCM64
 
0.2%
Other values (18)385
 
1.0%
(Missing)35147
93.6%

Length

2022-01-13T14:14:09.183163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sub1236
51.5%
rcb113
 
4.7%
lcb101
 
4.2%
gk98
 
4.1%
lb90
 
3.7%
rb88
 
3.7%
st77
 
3.2%
lm77
 
3.2%
rm72
 
3.0%
lcm64
 
2.7%
Other values (18)385
 
16.0%

Most occurring characters

ValueCountFrequency (%)
B1655
25.3%
S1366
20.9%
U1236
18.9%
L444
 
6.8%
R443
 
6.8%
C442
 
6.7%
M441
 
6.7%
D104
 
1.6%
G98
 
1.5%
K98
 
1.5%
Other values (4)223
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter6550
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B1655
25.3%
S1366
20.9%
U1236
18.9%
L444
 
6.8%
R443
 
6.8%
C442
 
6.7%
M441
 
6.7%
D104
 
1.6%
G98
 
1.5%
K98
 
1.5%
Other values (4)223
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Latin6550
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B1655
25.3%
S1366
20.9%
U1236
18.9%
L444
 
6.8%
R443
 
6.8%
C442
 
6.7%
M441
 
6.7%
D104
 
1.6%
G98
 
1.5%
K98
 
1.5%
Other values (4)223
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII6550
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B1655
25.3%
S1366
20.9%
U1236
18.9%
L444
 
6.8%
R443
 
6.8%
C442
 
6.7%
M441
 
6.7%
D104
 
1.6%
G98
 
1.5%
K98
 
1.5%
Other values (4)223
 
3.4%

nation_jersey_number
Real number (ℝ≥0)

MISSING

Distinct32
Distinct (%)1.3%
Missing35147
Missing (%)93.6%
Infinite0
Infinite (%)0.0%
Mean12.09204498
Minimum1
Maximum87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size293.5 KiB
2022-01-13T14:14:09.226180image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median12
Q318
95-th percentile22
Maximum87
Range86
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.848134115
Coefficient of variation (CV)0.5663338274
Kurtosis4.680136399
Mean12.09204498
Median Absolute Deviation (MAD)6
Skewness0.5570739633
Sum29033
Variance46.89694086
MonotonicityNot monotonic
2022-01-13T14:14:09.268288image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
7112
 
0.3%
13112
 
0.3%
19112
 
0.3%
15110
 
0.3%
10109
 
0.3%
17109
 
0.3%
8106
 
0.3%
12106
 
0.3%
3106
 
0.3%
1105
 
0.3%
Other values (22)1314
 
3.5%
(Missing)35147
93.6%
ValueCountFrequency (%)
1105
0.3%
2103
0.3%
3106
0.3%
498
0.3%
5104
0.3%
698
0.3%
7112
0.3%
8106
0.3%
996
0.3%
10109
0.3%
ValueCountFrequency (%)
871
 
< 0.1%
321
 
< 0.1%
302
 
< 0.1%
292
 
< 0.1%
281
 
< 0.1%
271
 
< 0.1%
262
 
< 0.1%
251
 
< 0.1%
245
 
< 0.1%
2392
0.2%

pace
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct74
Distinct (%)0.2%
Missing4192
Missing (%)11.2%
Infinite0
Infinite (%)0.0%
Mean67.88604749
Minimum23
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size293.5 KiB
2022-01-13T14:14:09.325411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile48
Q162
median69
Q376
95-th percentile85
Maximum96
Range73
Interquartile range (IQR)14

Descriptive statistics

Standard deviation11.27912159
Coefficient of variation (CV)0.1661478612
Kurtosis0.5710823573
Mean67.88604749
Median Absolute Deviation (MAD)7
Skewness-0.5511074726
Sum2264407
Variance127.2185839
MonotonicityNot monotonic
2022-01-13T14:14:09.378784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
671375
 
3.7%
661326
 
3.5%
681315
 
3.5%
711284
 
3.4%
691247
 
3.3%
701240
 
3.3%
741209
 
3.2%
721198
 
3.2%
731197
 
3.2%
651174
 
3.1%
Other values (64)20791
55.4%
(Missing)4192
 
11.2%
ValueCountFrequency (%)
232
 
< 0.1%
242
 
< 0.1%
253
 
< 0.1%
261
 
< 0.1%
272
 
< 0.1%
281
 
< 0.1%
2913
 
< 0.1%
3029
 
0.1%
3162
0.2%
3274
0.2%
ValueCountFrequency (%)
964
 
< 0.1%
958
 
< 0.1%
9427
 
0.1%
9364
 
0.2%
92107
0.3%
91171
0.5%
90199
0.5%
89196
0.5%
88218
0.6%
87246
0.7%

shooting
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct79
Distinct (%)0.2%
Missing4192
Missing (%)11.2%
Infinite0
Infinite (%)0.0%
Mean52.36874925
Minimum14
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size293.5 KiB
2022-01-13T14:14:09.433376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile27
Q142
median55
Q363
95-th percentile72
Maximum93
Range79
Interquartile range (IQR)21

Descriptive statistics

Standard deviation14.02335075
Coefficient of variation (CV)0.2677808989
Kurtosis-0.7490596442
Mean52.36874925
Median Absolute Deviation (MAD)10
Skewness-0.304331459
Sum1746812
Variance196.6543662
MonotonicityNot monotonic
2022-01-13T14:14:09.484747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
611031
 
2.7%
601026
 
2.7%
631007
 
2.7%
591000
 
2.7%
62993
 
2.6%
64976
 
2.6%
58934
 
2.5%
57923
 
2.5%
56919
 
2.4%
65899
 
2.4%
Other values (69)23648
63.0%
(Missing)4192
 
11.2%
ValueCountFrequency (%)
141
 
< 0.1%
154
 
< 0.1%
169
 
< 0.1%
1710
 
< 0.1%
1813
 
< 0.1%
1916
 
< 0.1%
2027
 
0.1%
2145
 
0.1%
2288
0.2%
23177
0.5%
ValueCountFrequency (%)
933
 
< 0.1%
911
 
< 0.1%
905
 
< 0.1%
893
 
< 0.1%
884
 
< 0.1%
876
 
< 0.1%
8610
 
< 0.1%
8515
< 0.1%
8425
0.1%
8325
0.1%

passing
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct68
Distinct (%)0.2%
Missing4192
Missing (%)11.2%
Infinite0
Infinite (%)0.0%
Mean57.15403526
Minimum24
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size293.5 KiB
2022-01-13T14:14:09.540877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile38
Q150
median58
Q364
95-th percentile74
Maximum92
Range68
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.58677595
Coefficient of variation (CV)0.1852323446
Kurtosis-0.1718463685
Mean57.15403526
Median Absolute Deviation (MAD)7
Skewness-0.2633392549
Sum1906430
Variance112.0798251
MonotonicityNot monotonic
2022-01-13T14:14:09.589309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
591361
 
3.6%
611339
 
3.6%
601319
 
3.5%
621295
 
3.4%
571289
 
3.4%
581270
 
3.4%
631236
 
3.3%
561209
 
3.2%
641133
 
3.0%
541102
 
2.9%
Other values (58)20803
55.4%
(Missing)4192
 
11.2%
ValueCountFrequency (%)
247
 
< 0.1%
259
 
< 0.1%
2620
 
0.1%
2728
 
0.1%
2862
0.2%
2977
0.2%
3066
0.2%
3180
0.2%
32105
0.3%
33128
0.3%
ValueCountFrequency (%)
921
 
< 0.1%
903
 
< 0.1%
893
 
< 0.1%
887
 
< 0.1%
877
 
< 0.1%
8613
 
< 0.1%
8514
 
< 0.1%
8431
0.1%
8330
0.1%
8247
0.1%

dribbling
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct72
Distinct (%)0.2%
Missing4192
Missing (%)11.2%
Infinite0
Infinite (%)0.0%
Mean62.35318983
Minimum23
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size293.5 KiB
2022-01-13T14:14:09.642913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile42
Q157
median64
Q369
95-th percentile77
Maximum96
Range73
Interquartile range (IQR)12

Descriptive statistics

Standard deviation10.45688992
Coefficient of variation (CV)0.1677041696
Kurtosis0.4131638844
Mean62.35318983
Median Absolute Deviation (MAD)6
Skewness-0.6023270847
Sum2079853
Variance109.3465469
MonotonicityNot monotonic
2022-01-13T14:14:09.692990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
651579
 
4.2%
641513
 
4.0%
661479
 
3.9%
671463
 
3.9%
631414
 
3.8%
681396
 
3.7%
621373
 
3.7%
691279
 
3.4%
611276
 
3.4%
701198
 
3.2%
Other values (62)19386
51.6%
(Missing)4192
 
11.2%
ValueCountFrequency (%)
231
 
< 0.1%
262
 
< 0.1%
272
 
< 0.1%
2815
 
< 0.1%
2929
 
0.1%
3053
0.1%
3170
0.2%
3278
0.2%
3383
0.2%
3483
0.2%
ValueCountFrequency (%)
962
 
< 0.1%
953
 
< 0.1%
942
 
< 0.1%
931
 
< 0.1%
922
 
< 0.1%
9110
 
< 0.1%
9011
 
< 0.1%
8923
0.1%
8811
 
< 0.1%
8735
0.1%

defending
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct77
Distinct (%)0.2%
Missing4192
Missing (%)11.2%
Infinite0
Infinite (%)0.0%
Mean51.3024943
Minimum15
Maximum91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size293.5 KiB
2022-01-13T14:14:09.748511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile23
Q136
median56
Q365
95-th percentile74
Maximum91
Range76
Interquartile range (IQR)29

Descriptive statistics

Standard deviation16.7665801
Coefficient of variation (CV)0.3268180295
Kurtosis-1.106418073
Mean51.3024943
Median Absolute Deviation (MAD)12
Skewness-0.3307302013
Sum1711246
Variance281.1182081
MonotonicityNot monotonic
2022-01-13T14:14:09.824174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
631166
 
3.1%
641092
 
2.9%
621067
 
2.8%
651053
 
2.8%
611025
 
2.7%
60996
 
2.7%
66988
 
2.6%
59907
 
2.4%
67861
 
2.3%
69799
 
2.1%
Other values (67)23402
62.3%
(Missing)4192
 
11.2%
ValueCountFrequency (%)
158
 
< 0.1%
1628
 
0.1%
1775
 
0.2%
18165
 
0.4%
19218
0.6%
20266
0.7%
21292
0.8%
22374
1.0%
23384
1.0%
24432
1.2%
ValueCountFrequency (%)
912
 
< 0.1%
903
 
< 0.1%
895
 
< 0.1%
888
 
< 0.1%
8710
 
< 0.1%
8619
 
0.1%
8527
0.1%
8426
0.1%
8346
0.1%
8259
0.2%

physic
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct63
Distinct (%)0.2%
Missing4192
Missing (%)11.2%
Infinite0
Infinite (%)0.0%
Mean64.94564696
Minimum27
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size293.5 KiB
2022-01-13T14:14:09.878807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile47
Q159
median66
Q372
95-th percentile79
Maximum92
Range65
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.770087422
Coefficient of variation (CV)0.150434831
Kurtosis-0.1144696176
Mean64.94564696
Median Absolute Deviation (MAD)7
Skewness-0.5006440082
Sum2166327
Variance95.45460824
MonotonicityNot monotonic
2022-01-13T14:14:09.932071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
701447
 
3.9%
711436
 
3.8%
691415
 
3.8%
671363
 
3.6%
681358
 
3.6%
721336
 
3.6%
661322
 
3.5%
651282
 
3.4%
641238
 
3.3%
731196
 
3.2%
Other values (53)19963
53.2%
(Missing)4192
 
11.2%
ValueCountFrequency (%)
272
 
< 0.1%
301
 
< 0.1%
312
 
< 0.1%
325
 
< 0.1%
3311
 
< 0.1%
3421
 
0.1%
3531
0.1%
3633
0.1%
3744
0.1%
3872
0.2%
ValueCountFrequency (%)
921
 
< 0.1%
903
 
< 0.1%
897
 
< 0.1%
8814
 
< 0.1%
8720
 
0.1%
8641
 
0.1%
8564
 
0.2%
84115
0.3%
83156
0.4%
82231
0.6%

gk_diving
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct49
Distinct (%)1.2%
Missing33356
Missing (%)88.8%
Infinite0
Infinite (%)0.0%
Mean65.33730916
Minimum39
Maximum91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size293.5 KiB
2022-01-13T14:14:09.985819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum39
5-th percentile53
Q160
median65
Q370
95-th percentile79
Maximum91
Range52
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.786292626
Coefficient of variation (CV)0.1191706963
Kurtosis-0.0729418125
Mean65.33730916
Median Absolute Deviation (MAD)5
Skewness0.1394722215
Sum273894
Variance60.62635286
MonotonicityNot monotonic
2022-01-13T14:14:10.038468image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
64248
 
0.7%
65223
 
0.6%
63219
 
0.6%
62213
 
0.6%
66212
 
0.6%
68209
 
0.6%
67197
 
0.5%
61184
 
0.5%
69170
 
0.5%
70169
 
0.5%
Other values (39)2148
 
5.7%
(Missing)33356
88.8%
ValueCountFrequency (%)
391
 
< 0.1%
441
 
< 0.1%
457
 
< 0.1%
4615
 
< 0.1%
4713
 
< 0.1%
4818
 
< 0.1%
4926
0.1%
5023
0.1%
5134
0.1%
5251
0.1%
ValueCountFrequency (%)
911
 
< 0.1%
903
 
< 0.1%
892
 
< 0.1%
885
 
< 0.1%
876
 
< 0.1%
8611
< 0.1%
8511
< 0.1%
8414
< 0.1%
8322
0.1%
8227
0.1%

gk_handling
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct49
Distinct (%)1.2%
Missing33356
Missing (%)88.8%
Infinite0
Infinite (%)0.0%
Mean62.94584924
Minimum43
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size293.5 KiB
2022-01-13T14:14:10.087630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile51
Q158
median63
Q368
95-th percentile76
Maximum92
Range49
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.561198779
Coefficient of variation (CV)0.1201222777
Kurtosis0.09200260467
Mean62.94584924
Median Absolute Deviation (MAD)5
Skewness0.2431170038
Sum263869
Variance57.17172698
MonotonicityNot monotonic
2022-01-13T14:14:10.138555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
62249
 
0.7%
60245
 
0.7%
65229
 
0.6%
58212
 
0.6%
64208
 
0.6%
66205
 
0.5%
59205
 
0.5%
61204
 
0.5%
63202
 
0.5%
67171
 
0.5%
Other values (39)2062
 
5.5%
(Missing)33356
88.8%
ValueCountFrequency (%)
434
 
< 0.1%
444
 
< 0.1%
4514
 
< 0.1%
4613
 
< 0.1%
4733
 
0.1%
4838
0.1%
4931
 
0.1%
5044
0.1%
5173
0.2%
5293
0.2%
ValueCountFrequency (%)
922
 
< 0.1%
912
 
< 0.1%
901
 
< 0.1%
882
 
< 0.1%
872
 
< 0.1%
864
 
< 0.1%
854
 
< 0.1%
8410
< 0.1%
8311
< 0.1%
8214
< 0.1%

gk_kicking
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct54
Distinct (%)1.3%
Missing33356
Missing (%)88.8%
Infinite0
Infinite (%)0.0%
Mean61.62428435
Minimum35
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size293.5 KiB
2022-01-13T14:14:10.189319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile50
Q156
median61
Q366
95-th percentile75
Maximum93
Range58
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.673535165
Coefficient of variation (CV)0.1245212865
Kurtosis0.2788842112
Mean61.62428435
Median Absolute Deviation (MAD)5
Skewness0.301634685
Sum258329
Variance58.88314194
MonotonicityNot monotonic
2022-01-13T14:14:10.239742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60247
 
0.7%
58239
 
0.6%
59228
 
0.6%
62220
 
0.6%
65219
 
0.6%
61212
 
0.6%
64209
 
0.6%
63185
 
0.5%
66183
 
0.5%
55175
 
0.5%
Other values (44)2075
 
5.5%
(Missing)33356
88.8%
ValueCountFrequency (%)
353
 
< 0.1%
391
 
< 0.1%
402
 
< 0.1%
412
 
< 0.1%
422
 
< 0.1%
432
 
< 0.1%
4415
< 0.1%
4517
< 0.1%
4630
0.1%
4731
0.1%
ValueCountFrequency (%)
931
 
< 0.1%
912
 
< 0.1%
905
< 0.1%
881
 
< 0.1%
874
< 0.1%
862
 
< 0.1%
852
 
< 0.1%
845
< 0.1%
833
 
< 0.1%
828
< 0.1%

gk_reflexes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct50
Distinct (%)1.2%
Missing33356
Missing (%)88.8%
Infinite0
Infinite (%)0.0%
Mean66.28339695
Minimum37
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size293.5 KiB
2022-01-13T14:14:10.307558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile53
Q160
median66
Q372
95-th percentile80
Maximum92
Range55
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.22226616
Coefficient of variation (CV)0.1240471451
Kurtosis-0.2248580549
Mean66.28339695
Median Absolute Deviation (MAD)6
Skewness0.1155886023
Sum277860
Variance67.6056608
MonotonicityNot monotonic
2022-01-13T14:14:10.357357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64219
 
0.6%
65206
 
0.5%
66202
 
0.5%
68201
 
0.5%
63195
 
0.5%
69193
 
0.5%
67177
 
0.5%
70173
 
0.5%
62169
 
0.5%
74164
 
0.4%
Other values (40)2293
 
6.1%
(Missing)33356
88.8%
ValueCountFrequency (%)
371
 
< 0.1%
443
 
< 0.1%
454
 
< 0.1%
4610
 
< 0.1%
4710
 
< 0.1%
4814
 
< 0.1%
4923
 
0.1%
5026
0.1%
5134
0.1%
5258
0.2%
ValueCountFrequency (%)
921
 
< 0.1%
911
 
< 0.1%
904
 
< 0.1%
896
 
< 0.1%
889
 
< 0.1%
8713
< 0.1%
8611
 
< 0.1%
8517
< 0.1%
8428
0.1%
8332
0.1%

gk_speed
Real number (ℝ≥0)

MISSING

Distinct54
Distinct (%)1.3%
Missing33356
Missing (%)88.8%
Infinite0
Infinite (%)0.0%
Mean38.59541985
Minimum12
Maximum65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size293.5 KiB
2022-01-13T14:14:10.409564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile21
Q130
median40
Q346
95-th percentile55
Maximum65
Range53
Interquartile range (IQR)16

Descriptive statistics

Standard deviation10.46047693
Coefficient of variation (CV)0.2710289712
Kurtosis-0.7169194866
Mean38.59541985
Median Absolute Deviation (MAD)8
Skewness-0.1259518726
Sum161792
Variance109.4215777
MonotonicityNot monotonic
2022-01-13T14:14:10.464947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45220
 
0.6%
43201
 
0.5%
44184
 
0.5%
46173
 
0.5%
48157
 
0.4%
42157
 
0.4%
41133
 
0.4%
47128
 
0.3%
38122
 
0.3%
40121
 
0.3%
Other values (44)2596
 
6.9%
(Missing)33356
88.8%
ValueCountFrequency (%)
122
 
< 0.1%
133
 
< 0.1%
142
 
< 0.1%
155
 
< 0.1%
1619
 
0.1%
1723
 
0.1%
1838
0.1%
1932
0.1%
2032
0.1%
2160
0.2%
ValueCountFrequency (%)
654
 
< 0.1%
643
 
< 0.1%
6314
 
< 0.1%
622
 
< 0.1%
6111
 
< 0.1%
6031
0.1%
5917
 
< 0.1%
5830
0.1%
5734
0.1%
5653
0.1%

gk_positioning
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct54
Distinct (%)1.3%
Missing33356
Missing (%)88.8%
Infinite0
Infinite (%)0.0%
Mean63.18821565
Minimum38
Maximum91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size293.5 KiB
2022-01-13T14:14:10.514958image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum38
5-th percentile49
Q158
median64
Q369
95-th percentile77
Maximum91
Range53
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.631960193
Coefficient of variation (CV)0.1366071206
Kurtosis-0.1337349555
Mean63.18821565
Median Absolute Deviation (MAD)6
Skewness-0.02840629127
Sum264885
Variance74.51073678
MonotonicityNot monotonic
2022-01-13T14:14:10.565363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64234
 
0.6%
65231
 
0.6%
63213
 
0.6%
66189
 
0.5%
67186
 
0.5%
62184
 
0.5%
58179
 
0.5%
61174
 
0.5%
68174
 
0.5%
60168
 
0.4%
Other values (44)2260
 
6.0%
(Missing)33356
88.8%
ValueCountFrequency (%)
381
 
< 0.1%
391
 
< 0.1%
4010
 
< 0.1%
417
 
< 0.1%
4210
 
< 0.1%
4317
< 0.1%
4422
0.1%
4526
0.1%
4625
0.1%
4741
0.1%
ValueCountFrequency (%)
911
 
< 0.1%
904
 
< 0.1%
892
 
< 0.1%
882
 
< 0.1%
873
 
< 0.1%
865
 
< 0.1%
857
 
< 0.1%
845
 
< 0.1%
837
 
< 0.1%
8231
0.1%

player_traits
Categorical

HIGH CARDINALITY
MISSING

Distinct2310
Distinct (%)13.9%
Missing20881
Missing (%)55.6%
Memory size2.1 MiB
Injury Prone
 
632
Speed Dribbler (CPU AI Only)
 
577
Early Crosser
 
532
Long Passer (CPU AI Only)
 
508
Dives Into Tackles (CPU AI Only)
 
450
Other values (2305)
13968 

Length

Max length167
Median length28
Mean length34.07463851
Min length5

Characters and Unicode

Total characters567922
Distinct characters48
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1227 ?
Unique (%)7.4%

Sample

1st rowLong Passer (CPU AI Only), Long Shot Taker (CPU AI Only), Playmaker (CPU AI Only)
2nd rowAvoids Using Weaker Foot, Early Crosser
3rd rowSelfish, Argues with Officials, Crowd Favourite, Skilled Dribbling
4th rowPower Header
5th rowFinesse Shot, Flair, Playmaker (CPU AI Only), Technical Dribbler (CPU AI Only)

Common Values

ValueCountFrequency (%)
Injury Prone632
 
1.7%
Speed Dribbler (CPU AI Only)577
 
1.5%
Early Crosser532
 
1.4%
Long Passer (CPU AI Only)508
 
1.4%
Dives Into Tackles (CPU AI Only)450
 
1.2%
Technical Dribbler (CPU AI Only)363
 
1.0%
Power Free-Kick330
 
0.9%
Long Shot Taker (CPU AI Only)318
 
0.8%
Power Header303
 
0.8%
Leadership297
 
0.8%
Other values (2300)12357
32.9%
(Missing)20881
55.6%

Length

2022-01-13T14:14:10.689940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ai8731
 
9.5%
cpu8731
 
9.5%
only8731
 
9.5%
long3748
 
4.1%
dribbler3583
 
3.9%
shot2881
 
3.1%
injury2796
 
3.1%
power1979
 
2.2%
speed1802
 
2.0%
prone1799
 
2.0%
Other values (74)46834
51.1%

Most occurring characters

ValueCountFrequency (%)
74948
 
13.2%
e48098
 
8.5%
r39836
 
7.0%
n25867
 
4.6%
i25286
 
4.5%
a24221
 
4.3%
l24094
 
4.2%
s23626
 
4.2%
o22906
 
4.0%
P16638
 
2.9%
Other values (38)242402
42.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter341195
60.1%
Uppercase Letter118078
 
20.8%
Space Separator74948
 
13.2%
Other Punctuation14064
 
2.5%
Open Punctuation8731
 
1.5%
Close Punctuation8731
 
1.5%
Dash Punctuation2169
 
0.4%
Decimal Number6
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e48098
14.1%
r39836
11.7%
n25867
 
7.6%
i25286
 
7.4%
a24221
 
7.1%
l24094
 
7.1%
s23626
 
6.9%
o22906
 
6.7%
y15163
 
4.4%
t11553
 
3.4%
Other values (14)80545
23.6%
Uppercase Letter
ValueCountFrequency (%)
P16638
14.1%
I13281
11.2%
C12485
10.6%
O11219
9.5%
A10866
9.2%
U9948
8.4%
F9040
7.7%
T8933
7.6%
S7053
6.0%
D5422
 
4.6%
Other values (8)13193
11.2%
Space Separator
ValueCountFrequency (%)
74948
100.0%
Open Punctuation
ValueCountFrequency (%)
(8731
100.0%
Close Punctuation
ValueCountFrequency (%)
)8731
100.0%
Other Punctuation
ValueCountFrequency (%)
,14064
100.0%
Dash Punctuation
ValueCountFrequency (%)
-2169
100.0%
Decimal Number
ValueCountFrequency (%)
16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin459273
80.9%
Common108649
 
19.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e48098
 
10.5%
r39836
 
8.7%
n25867
 
5.6%
i25286
 
5.5%
a24221
 
5.3%
l24094
 
5.2%
s23626
 
5.1%
o22906
 
5.0%
P16638
 
3.6%
y15163
 
3.3%
Other values (32)193538
42.1%
Common
ValueCountFrequency (%)
74948
69.0%
,14064
 
12.9%
(8731
 
8.0%
)8731
 
8.0%
-2169
 
2.0%
16
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII567922
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
74948
 
13.2%
e48098
 
8.5%
r39836
 
7.0%
n25867
 
4.6%
i25286
 
4.5%
a24221
 
4.3%
l24094
 
4.2%
s23626
 
4.2%
o22906
 
4.0%
P16638
 
2.9%
Other values (38)242402
42.7%

attacking_crossing
Categorical

HIGH CARDINALITY

Distinct721
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
62
 
1034
60
 
1006
58
 
982
59
 
975
64
 
960
Other values (716)
32591 

Length

Max length5
Median length2
Mean length2.080909769
Min length1

Characters and Unicode

Total characters78134
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique339 ?
Unique (%)0.9%

Sample

1st row76
2nd row68
3rd row64
4th row40
5th row41

Common Values

ValueCountFrequency (%)
621034
 
2.8%
601006
 
2.7%
58982
 
2.6%
59975
 
2.6%
64960
 
2.6%
65917
 
2.4%
61907
 
2.4%
63902
 
2.4%
57880
 
2.3%
66843
 
2.2%
Other values (711)28142
74.9%

Length

2022-01-13T14:14:10.814562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
621034
 
2.8%
601006
 
2.7%
58982
 
2.6%
59975
 
2.6%
64960
 
2.6%
65917
 
2.4%
61907
 
2.4%
63902
 
2.4%
57880
 
2.3%
66843
 
2.2%
Other values (711)28142
74.9%

Most occurring characters

ValueCountFrequency (%)
612986
16.6%
512108
15.5%
49418
12.1%
17953
10.2%
37925
10.1%
77370
9.4%
26880
8.8%
84339
 
5.6%
03984
 
5.1%
93660
 
4.7%
Other values (2)1511
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number76623
98.1%
Math Symbol1066
 
1.4%
Dash Punctuation445
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
612986
16.9%
512108
15.8%
49418
12.3%
17953
10.4%
37925
10.3%
77370
9.6%
26880
9.0%
84339
 
5.7%
03984
 
5.2%
93660
 
4.8%
Math Symbol
ValueCountFrequency (%)
+1066
100.0%
Dash Punctuation
ValueCountFrequency (%)
-445
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common78134
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
612986
16.6%
512108
15.5%
49418
12.1%
17953
10.2%
37925
10.1%
77370
9.4%
26880
8.8%
84339
 
5.6%
03984
 
5.1%
93660
 
4.7%
Other values (2)1511
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII78134
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
612986
16.6%
512108
15.5%
49418
12.1%
17953
10.2%
37925
10.1%
77370
9.4%
26880
8.8%
84339
 
5.6%
03984
 
5.1%
93660
 
4.7%
Other values (2)1511
 
1.9%

attacking_finishing
Categorical

HIGH CARDINALITY

Distinct771
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
58
 
917
65
 
858
62
 
834
60
 
832
59
 
815
Other values (766)
33292 

Length

Max length5
Median length2
Mean length2.062480026
Min length1

Characters and Unicode

Total characters77442
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique354 ?
Unique (%)0.9%

Sample

1st row73
2nd row48
3rd row73
4th row72
5th row33

Common Values

ValueCountFrequency (%)
58917
 
2.4%
65858
 
2.3%
62834
 
2.2%
60832
 
2.2%
59815
 
2.2%
64760
 
2.0%
63746
 
2.0%
66730
 
1.9%
55727
 
1.9%
61711
 
1.9%
Other values (761)29618
78.9%

Length

2022-01-13T14:14:10.924701image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
58917
 
2.4%
65858
 
2.3%
62834
 
2.2%
60832
 
2.2%
59815
 
2.2%
64760
 
2.0%
63746
 
2.0%
66730
 
1.9%
55727
 
1.9%
61711
 
1.9%
Other values (761)29618
78.9%

Most occurring characters

ValueCountFrequency (%)
611458
14.8%
511340
14.6%
48916
11.5%
28824
11.4%
38719
11.3%
17840
10.1%
76448
8.3%
84536
 
5.9%
03855
 
5.0%
93782
 
4.9%
Other values (2)1724
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number75718
97.8%
Math Symbol1073
 
1.4%
Dash Punctuation651
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
611458
15.1%
511340
15.0%
48916
11.8%
28824
11.7%
38719
11.5%
17840
10.4%
76448
8.5%
84536
 
6.0%
03855
 
5.1%
93782
 
5.0%
Math Symbol
ValueCountFrequency (%)
+1073
100.0%
Dash Punctuation
ValueCountFrequency (%)
-651
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common77442
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
611458
14.8%
511340
14.6%
48916
11.5%
28824
11.4%
38719
11.3%
17840
10.1%
76448
8.3%
84536
 
5.9%
03855
 
5.0%
93782
 
4.9%
Other values (2)1724
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII77442
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
611458
14.8%
511340
14.6%
48916
11.5%
28824
11.4%
38719
11.3%
17840
10.1%
76448
8.3%
84536
 
5.9%
03855
 
5.0%
93782
 
4.9%
Other values (2)1724
 
2.2%

attacking_heading_accuracy
Categorical

HIGH CARDINALITY

Distinct662
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
58
 
1236
60
 
1159
59
 
1106
55
 
1081
65
 
1008
Other values (657)
31958 

Length

Max length5
Median length2
Mean length2.075876212
Min length1

Characters and Unicode

Total characters77945
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique296 ?
Unique (%)0.8%

Sample

1st row57
2nd row60
3rd row59
4th row73
5th row55

Common Values

ValueCountFrequency (%)
581236
 
3.3%
601159
 
3.1%
591106
 
2.9%
551081
 
2.9%
651008
 
2.7%
62997
 
2.7%
64975
 
2.6%
56940
 
2.5%
54930
 
2.5%
57922
 
2.5%
Other values (652)27194
72.4%

Length

2022-01-13T14:14:11.039613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
581236
 
3.3%
601159
 
3.1%
591106
 
2.9%
551081
 
2.9%
651008
 
2.7%
62997
 
2.7%
64975
 
2.6%
56940
 
2.5%
54930
 
2.5%
57922
 
2.5%
Other values (652)27194
72.4%

Most occurring characters

ValueCountFrequency (%)
513839
17.8%
613107
16.8%
410708
13.7%
77677
9.8%
17609
9.8%
36250
8.0%
25046
 
6.5%
84670
 
6.0%
03982
 
5.1%
93627
 
4.7%
Other values (2)1430
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number76515
98.2%
Math Symbol955
 
1.2%
Dash Punctuation475
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
513839
18.1%
613107
17.1%
410708
14.0%
77677
10.0%
17609
9.9%
36250
8.2%
25046
 
6.6%
84670
 
6.1%
03982
 
5.2%
93627
 
4.7%
Dash Punctuation
ValueCountFrequency (%)
-475
100.0%
Math Symbol
ValueCountFrequency (%)
+955
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common77945
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
513839
17.8%
613107
16.8%
410708
13.7%
77677
9.8%
17609
9.8%
36250
8.0%
25046
 
6.5%
84670
 
6.0%
03982
 
5.1%
93627
 
4.7%
Other values (2)1430
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII77945
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
513839
17.8%
613107
16.8%
410708
13.7%
77677
9.8%
17609
9.8%
36250
8.0%
25046
 
6.5%
84670
 
6.0%
03982
 
5.1%
93627
 
4.7%
Other values (2)1430
 
1.8%

attacking_short_passing
Categorical

HIGH CARDINALITY

Distinct708
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
64
 
1584
65
 
1554
62
 
1464
63
 
1435
68
 
1408
Other values (703)
30103 

Length

Max length5
Median length2
Mean length2.125173112
Min length1

Characters and Unicode

Total characters79796
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique312 ?
Unique (%)0.8%

Sample

1st row80
2nd row72-3
3rd row72
4th row62
5th row54

Common Values

ValueCountFrequency (%)
641584
 
4.2%
651554
 
4.1%
621464
 
3.9%
631435
 
3.8%
681408
 
3.7%
661395
 
3.7%
671308
 
3.5%
601192
 
3.2%
581126
 
3.0%
591088
 
2.9%
Other values (698)23994
63.9%

Length

2022-01-13T14:14:11.161017image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
641584
 
4.2%
651554
 
4.1%
621464
 
3.9%
631435
 
3.8%
681408
 
3.7%
661395
 
3.7%
671308
 
3.5%
601192
 
3.2%
581126
 
3.0%
591088
 
2.9%
Other values (698)23994
63.9%

Most occurring characters

ValueCountFrequency (%)
618308
22.9%
512175
15.3%
710573
13.3%
26972
 
8.7%
46817
 
8.5%
35920
 
7.4%
84902
 
6.1%
14634
 
5.8%
93610
 
4.5%
03603
 
4.5%
Other values (2)2282
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number77514
97.1%
Math Symbol1539
 
1.9%
Dash Punctuation743
 
0.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
618308
23.6%
512175
15.7%
710573
13.6%
26972
 
9.0%
46817
 
8.8%
35920
 
7.6%
84902
 
6.3%
14634
 
6.0%
93610
 
4.7%
03603
 
4.6%
Dash Punctuation
ValueCountFrequency (%)
-743
100.0%
Math Symbol
ValueCountFrequency (%)
+1539
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common79796
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
618308
22.9%
512175
15.3%
710573
13.3%
26972
 
8.7%
46817
 
8.5%
35920
 
7.4%
84902
 
6.1%
14634
 
5.8%
93610
 
4.5%
03603
 
4.5%
Other values (2)2282
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII79796
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
618308
22.9%
512175
15.3%
710573
13.3%
26972
 
8.7%
46817
 
8.5%
35920
 
7.4%
84902
 
6.1%
14634
 
5.8%
93610
 
4.5%
03603
 
4.5%
Other values (2)2282
 
2.9%

attacking_volleys
Categorical

HIGH CARDINALITY

Distinct602
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
48
 
814
49
 
802
59
 
786
55
 
782
45
 
764
Other values (597)
33600 

Length

Max length5
Median length2
Mean length2.012011292
Min length1

Characters and Unicode

Total characters75547
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique355 ?
Unique (%)0.9%

Sample

1st row78
2nd row57
3rd row71
4th row62
5th row31

Common Values

ValueCountFrequency (%)
48814
 
2.2%
49802
 
2.1%
59786
 
2.1%
55782
 
2.1%
45764
 
2.0%
42750
 
2.0%
52744
 
2.0%
58736
 
2.0%
44735
 
2.0%
53724
 
1.9%
Other values (592)29911
79.7%

Length

2022-01-13T14:14:11.260735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
48814
 
2.2%
49802
 
2.1%
59786
 
2.1%
55782
 
2.1%
45764
 
2.0%
42750
 
2.0%
52744
 
2.0%
58736
 
2.0%
44735
 
2.0%
53724
 
1.9%
Other values (592)29911
79.7%

Most occurring characters

ValueCountFrequency (%)
511418
15.1%
411230
14.9%
310411
13.8%
68836
11.7%
28616
11.4%
16858
9.1%
75458
7.2%
84317
 
5.7%
93906
 
5.2%
03732
 
4.9%
Other values (2)765
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number74782
99.0%
Math Symbol513
 
0.7%
Dash Punctuation252
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
511418
15.3%
411230
15.0%
310411
13.9%
68836
11.8%
28616
11.5%
16858
9.2%
75458
7.3%
84317
 
5.8%
93906
 
5.2%
03732
 
5.0%
Math Symbol
ValueCountFrequency (%)
+513
100.0%
Dash Punctuation
ValueCountFrequency (%)
-252
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common75547
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
511418
15.1%
411230
14.9%
310411
13.8%
68836
11.7%
28616
11.4%
16858
9.1%
75458
7.2%
84317
 
5.7%
93906
 
5.2%
03732
 
4.9%
Other values (2)765
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII75547
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
511418
15.1%
411230
14.9%
310411
13.8%
68836
11.7%
28616
11.4%
16858
9.1%
75458
7.2%
84317
 
5.7%
93906
 
5.2%
03732
 
4.9%
Other values (2)765
 
1.0%

skill_dribbling
Categorical

HIGH CARDINALITY

Distinct724
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
65
 
1337
64
 
1304
63
 
1272
62
 
1263
66
 
1198
Other values (719)
31174 

Length

Max length5
Median length2
Mean length2.105491637
Min length1

Characters and Unicode

Total characters79057
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique297 ?
Unique (%)0.8%

Sample

1st row76
2nd row67
3rd row78
4th row59
5th row45

Common Values

ValueCountFrequency (%)
651337
 
3.6%
641304
 
3.5%
631272
 
3.4%
621263
 
3.4%
661198
 
3.2%
681179
 
3.1%
671145
 
3.0%
601031
 
2.7%
581030
 
2.7%
611024
 
2.7%
Other values (714)25765
68.6%

Length

2022-01-13T14:14:11.382140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
651337
 
3.6%
641304
 
3.5%
631272
 
3.4%
621263
 
3.4%
661198
 
3.2%
681179
 
3.1%
671145
 
3.0%
601031
 
2.7%
581030
 
2.7%
611024
 
2.7%
Other values (714)25765
68.6%

Most occurring characters

ValueCountFrequency (%)
616214
20.5%
511606
14.7%
710353
13.1%
17693
9.7%
47142
9.0%
26010
 
7.6%
35745
 
7.3%
84976
 
6.3%
03619
 
4.6%
93513
 
4.4%
Other values (2)2186
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number76871
97.2%
Math Symbol1468
 
1.9%
Dash Punctuation718
 
0.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
616214
21.1%
511606
15.1%
710353
13.5%
17693
10.0%
47142
9.3%
26010
 
7.8%
35745
 
7.5%
84976
 
6.5%
03619
 
4.7%
93513
 
4.6%
Math Symbol
ValueCountFrequency (%)
+1468
100.0%
Dash Punctuation
ValueCountFrequency (%)
-718
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common79057
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
616214
20.5%
511606
14.7%
710353
13.1%
17693
9.7%
47142
9.0%
26010
 
7.6%
35745
 
7.3%
84976
 
6.3%
03619
 
4.6%
93513
 
4.4%
Other values (2)2186
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII79057
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
616214
20.5%
511606
14.7%
710353
13.1%
17693
9.7%
47142
9.0%
26010
 
7.6%
35745
 
7.3%
84976
 
6.3%
03619
 
4.6%
93513
 
4.4%
Other values (2)2186
 
2.8%

skill_curve
Categorical

HIGH CARDINALITY

Distinct668
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
48
 
818
58
 
809
45
 
792
60
 
776
55
 
753
Other values (663)
33600 

Length

Max length5
Median length2
Mean length2.050841589
Min length1

Characters and Unicode

Total characters77005
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique387 ?
Unique (%)1.0%

Sample

1st row84
2nd row69
3rd row70
4th row62
5th row33

Common Values

ValueCountFrequency (%)
48818
 
2.2%
58809
 
2.2%
45792
 
2.1%
60776
 
2.1%
55753
 
2.0%
49746
 
2.0%
64743
 
2.0%
42729
 
1.9%
59727
 
1.9%
53712
 
1.9%
Other values (658)29943
79.7%

Length

2022-01-13T14:14:11.486708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
48818
 
2.2%
58809
 
2.2%
45792
 
2.1%
60776
 
2.1%
55753
 
2.0%
49746
 
2.0%
64743
 
2.0%
42729
 
1.9%
59727
 
1.9%
53712
 
1.9%
Other values (658)29943
79.7%

Most occurring characters

ValueCountFrequency (%)
411014
14.3%
510914
14.2%
610546
13.7%
39647
12.5%
17685
10.0%
27281
9.5%
76705
8.7%
84664
6.1%
03913
 
5.1%
93719
 
4.8%
Other values (2)917
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number76088
98.8%
Math Symbol710
 
0.9%
Dash Punctuation207
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
411014
14.5%
510914
14.3%
610546
13.9%
39647
12.7%
17685
10.1%
27281
9.6%
76705
8.8%
84664
6.1%
03913
 
5.1%
93719
 
4.9%
Math Symbol
ValueCountFrequency (%)
+710
100.0%
Dash Punctuation
ValueCountFrequency (%)
-207
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common77005
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
411014
14.3%
510914
14.2%
610546
13.7%
39647
12.5%
17685
10.0%
27281
9.5%
76705
8.7%
84664
6.1%
03913
 
5.1%
93719
 
4.8%
Other values (2)917
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII77005
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
411014
14.3%
510914
14.2%
610546
13.7%
39647
12.5%
17685
10.0%
27281
9.5%
76705
8.7%
84664
6.1%
03913
 
5.1%
93719
 
4.8%
Other values (2)917
 
1.2%

skill_fk_accuracy
Categorical

HIGH CARDINALITY

Distinct568
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
42
 
947
40
 
945
32
 
936
39
 
926
35
 
909
Other values (563)
32885 

Length

Max length5
Median length2
Mean length2.034063066
Min length1

Characters and Unicode

Total characters76375
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique385 ?
Unique (%)1.0%

Sample

1st row74
2nd row68
3rd row65
4th row45
5th row39

Common Values

ValueCountFrequency (%)
42947
 
2.5%
40945
 
2.5%
32936
 
2.5%
39926
 
2.5%
35909
 
2.4%
30845
 
2.3%
34833
 
2.2%
33832
 
2.2%
31831
 
2.2%
38811
 
2.2%
Other values (558)28733
76.5%

Length

2022-01-13T14:14:11.588308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
42947
 
2.5%
40945
 
2.5%
32936
 
2.5%
39926
 
2.5%
35909
 
2.4%
30845
 
2.3%
34833
 
2.2%
33832
 
2.2%
31831
 
2.2%
38811
 
2.2%
Other values (558)28733
76.5%

Most occurring characters

ValueCountFrequency (%)
312315
16.1%
411079
14.5%
59488
12.4%
28889
11.6%
68361
10.9%
17874
10.3%
75715
7.5%
84182
 
5.5%
04080
 
5.3%
93763
 
4.9%
Other values (2)629
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number75746
99.2%
Math Symbol418
 
0.5%
Dash Punctuation211
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
312315
16.3%
411079
14.6%
59488
12.5%
28889
11.7%
68361
11.0%
17874
10.4%
75715
7.5%
84182
 
5.5%
04080
 
5.4%
93763
 
5.0%
Math Symbol
ValueCountFrequency (%)
+418
100.0%
Dash Punctuation
ValueCountFrequency (%)
-211
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common76375
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
312315
16.1%
411079
14.5%
59488
12.4%
28889
11.6%
68361
10.9%
17874
10.3%
75715
7.5%
84182
 
5.5%
04080
 
5.3%
93763
 
4.9%
Other values (2)629
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII76375
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
312315
16.1%
411079
14.5%
59488
12.4%
28889
11.6%
68361
10.9%
17874
10.3%
75715
7.5%
84182
 
5.5%
04080
 
5.3%
93763
 
4.9%
Other values (2)629
 
0.8%

skill_long_passing
Categorical

HIGH CARDINALITY

Distinct759
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
59
 
1205
58
 
1205
62
 
1186
65
 
1122
63
 
1103
Other values (754)
31727 

Length

Max length5
Median length2
Mean length2.097927986
Min length1

Characters and Unicode

Total characters78773
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique359 ?
Unique (%)1.0%

Sample

1st row80
2nd row69-2
3rd row60
4th row52
5th row56

Common Values

ValueCountFrequency (%)
591205
 
3.2%
581205
 
3.2%
621186
 
3.2%
651122
 
3.0%
631103
 
2.9%
601095
 
2.9%
551075
 
2.9%
641058
 
2.8%
611010
 
2.7%
57974
 
2.6%
Other values (749)26515
70.6%

Length

2022-01-13T14:14:11.708570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
591205
 
3.2%
581205
 
3.2%
621186
 
3.2%
651122
 
3.0%
631103
 
2.9%
601095
 
2.9%
551075
 
2.9%
641058
 
2.8%
611010
 
2.7%
57974
 
2.6%
Other values (749)26515
70.6%

Most occurring characters

ValueCountFrequency (%)
513948
17.7%
613905
17.7%
49383
11.9%
27769
9.9%
37722
9.8%
77353
9.3%
15002
 
6.3%
84443
 
5.6%
93783
 
4.8%
03711
 
4.7%
Other values (2)1754
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number77019
97.8%
Math Symbol1263
 
1.6%
Dash Punctuation491
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
513948
18.1%
613905
18.1%
49383
12.2%
27769
10.1%
37722
10.0%
77353
9.5%
15002
 
6.5%
84443
 
5.8%
93783
 
4.9%
03711
 
4.8%
Dash Punctuation
ValueCountFrequency (%)
-491
100.0%
Math Symbol
ValueCountFrequency (%)
+1263
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common78773
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
513948
17.7%
613905
17.7%
49383
11.9%
27769
9.9%
37722
9.8%
77353
9.3%
15002
 
6.3%
84443
 
5.6%
93783
 
4.8%
03711
 
4.7%
Other values (2)1754
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII78773
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
513948
17.7%
613905
17.7%
49383
11.9%
27769
9.9%
37722
9.8%
77353
9.3%
15002
 
6.3%
84443
 
5.6%
93783
 
4.8%
03711
 
4.7%
Other values (2)1754
 
2.2%

skill_ball_control
Categorical

HIGH CARDINALITY

Distinct713
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
65
 
1530
64
 
1518
63
 
1422
66
 
1397
68
 
1381
Other values (708)
30300 

Length

Max length5
Median length2
Mean length2.133029722
Min length1

Characters and Unicode

Total characters80091
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique281 ?
Unique (%)0.7%

Sample

1st row81
2nd row74
3rd row77
4th row69
5th row54

Common Values

ValueCountFrequency (%)
651530
 
4.1%
641518
 
4.0%
631422
 
3.8%
661397
 
3.7%
681381
 
3.7%
621351
 
3.6%
671260
 
3.4%
601212
 
3.2%
701207
 
3.2%
611103
 
2.9%
Other values (703)24167
64.4%

Length

2022-01-13T14:14:11.823475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
651530
 
4.1%
641518
 
4.0%
631422
 
3.8%
661397
 
3.7%
681381
 
3.7%
621351
 
3.6%
671260
 
3.4%
601212
 
3.2%
701207
 
3.2%
611103
 
2.9%
Other values (703)24167
64.4%

Most occurring characters

ValueCountFrequency (%)
617948
22.4%
511313
14.1%
711110
13.9%
26699
 
8.4%
46455
 
8.1%
16263
 
7.8%
35245
 
6.5%
85087
 
6.4%
04116
 
5.1%
93407
 
4.3%
Other values (2)2448
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number77643
96.9%
Math Symbol1599
 
2.0%
Dash Punctuation849
 
1.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
617948
23.1%
511313
14.6%
711110
14.3%
26699
 
8.6%
46455
 
8.3%
16263
 
8.1%
35245
 
6.8%
85087
 
6.6%
04116
 
5.3%
93407
 
4.4%
Math Symbol
ValueCountFrequency (%)
+1599
100.0%
Dash Punctuation
ValueCountFrequency (%)
-849
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common80091
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
617948
22.4%
511313
14.1%
711110
13.9%
26699
 
8.4%
46455
 
8.1%
16263
 
7.8%
35245
 
6.5%
85087
 
6.4%
04116
 
5.1%
93407
 
4.3%
Other values (2)2448
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII80091
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
617948
22.4%
511313
14.1%
711110
13.9%
26699
 
8.4%
46455
 
8.1%
16263
 
7.8%
35245
 
6.5%
85087
 
6.4%
04116
 
5.1%
93407
 
4.3%
Other values (2)2448
 
3.1%

movement_acceleration
Categorical

HIGH CARDINALITY

Distinct976
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
68
 
1326
69
 
1233
67
 
1214
66
 
1138
65
 
1085
Other values (971)
31552 

Length

Max length5
Median length2
Mean length2.233141579
Min length2

Characters and Unicode

Total characters83850
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique365 ?
Unique (%)1.0%

Sample

1st row52
2nd row69
3rd row82
4th row54
5th row58

Common Values

ValueCountFrequency (%)
681326
 
3.5%
691233
 
3.3%
671214
 
3.2%
661138
 
3.0%
651085
 
2.9%
741021
 
2.7%
751017
 
2.7%
72991
 
2.6%
64975
 
2.6%
76974
 
2.6%
Other values (966)26574
70.8%

Length

2022-01-13T14:14:11.927645image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
681326
 
3.5%
691233
 
3.3%
671214
 
3.2%
661138
 
3.0%
651085
 
2.9%
741021
 
2.7%
751017
 
2.7%
72991
 
2.6%
64975
 
2.6%
76974
 
2.6%
Other values (966)26574
70.8%

Most occurring characters

ValueCountFrequency (%)
615092
18.0%
714796
17.6%
59548
11.4%
87975
9.5%
47240
8.6%
36424
7.7%
25437
 
6.5%
15143
 
6.1%
94555
 
5.4%
03377
 
4.0%
Other values (2)4263
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number79587
94.9%
Dash Punctuation3189
 
3.8%
Math Symbol1074
 
1.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
615092
19.0%
714796
18.6%
59548
12.0%
87975
10.0%
47240
9.1%
36424
8.1%
25437
 
6.8%
15143
 
6.5%
94555
 
5.7%
03377
 
4.2%
Dash Punctuation
ValueCountFrequency (%)
-3189
100.0%
Math Symbol
ValueCountFrequency (%)
+1074
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common83850
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
615092
18.0%
714796
17.6%
59548
11.4%
87975
9.5%
47240
8.6%
36424
7.7%
25437
 
6.5%
15143
 
6.1%
94555
 
5.4%
03377
 
4.0%
Other values (2)4263
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII83850
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
615092
18.0%
714796
17.6%
59548
11.4%
87975
9.5%
47240
8.6%
36424
7.7%
25437
 
6.5%
15143
 
6.1%
94555
 
5.4%
03377
 
4.0%
Other values (2)4263
 
5.1%

movement_sprint_speed
Categorical

HIGH CARDINALITY

Distinct981
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
68
 
1309
69
 
1283
67
 
1259
66
 
1157
73
 
1100
Other values (976)
31440 

Length

Max length5
Median length2
Mean length2.23439331
Min length2

Characters and Unicode

Total characters83897
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique403 ?
Unique (%)1.1%

Sample

1st row53
2nd row71
3rd row74
4th row51
5th row57

Common Values

ValueCountFrequency (%)
681309
 
3.5%
691283
 
3.4%
671259
 
3.4%
661157
 
3.1%
731100
 
2.9%
751093
 
2.9%
651082
 
2.9%
721057
 
2.8%
741043
 
2.8%
76971
 
2.6%
Other values (971)26194
69.8%

Length

2022-01-13T14:14:12.031101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
681309
 
3.5%
691283
 
3.4%
671259
 
3.4%
661157
 
3.1%
731100
 
2.9%
751093
 
2.9%
651082
 
2.9%
721057
 
2.8%
741043
 
2.8%
76971
 
2.6%
Other values (971)26194
69.8%

Most occurring characters

ValueCountFrequency (%)
615200
18.1%
714960
17.8%
59557
11.4%
87790
9.3%
47230
8.6%
36366
7.6%
25423
 
6.5%
15232
 
6.2%
94421
 
5.3%
03457
 
4.1%
Other values (2)4261
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number79636
94.9%
Dash Punctuation3186
 
3.8%
Math Symbol1075
 
1.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
615200
19.1%
714960
18.8%
59557
12.0%
87790
9.8%
47230
9.1%
36366
8.0%
25423
 
6.8%
15232
 
6.6%
94421
 
5.6%
03457
 
4.3%
Math Symbol
ValueCountFrequency (%)
+1075
100.0%
Dash Punctuation
ValueCountFrequency (%)
-3186
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common83897
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
615200
18.1%
714960
17.8%
59557
11.4%
87790
9.3%
47230
8.6%
36366
7.6%
25423
 
6.5%
15232
 
6.2%
94421
 
5.3%
03457
 
4.1%
Other values (2)4261
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII83897
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
615200
18.1%
714960
17.8%
59557
11.4%
87790
9.3%
47230
8.6%
36366
7.6%
25423
 
6.5%
15232
 
6.2%
94421
 
5.3%
03457
 
4.1%
Other values (2)4261
 
5.1%

movement_agility
Categorical

HIGH CARDINALITY

Distinct888
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
68
 
1083
70
 
1075
72
 
1054
71
 
1038
67
 
1019
Other values (883)
32279 

Length

Max length5
Median length2
Mean length2.179423671
Min length2

Characters and Unicode

Total characters81833
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique358 ?
Unique (%)1.0%

Sample

1st row67
2nd row69
3rd row90
4th row73
5th row54

Common Values

ValueCountFrequency (%)
681083
 
2.9%
701075
 
2.9%
721054
 
2.8%
711038
 
2.8%
671019
 
2.7%
731011
 
2.7%
691011
 
2.7%
65983
 
2.6%
66962
 
2.6%
75942
 
2.5%
Other values (878)27370
72.9%

Length

2022-01-13T14:14:12.144393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
681083
 
2.9%
701075
 
2.9%
721054
 
2.8%
711038
 
2.8%
671019
 
2.7%
731011
 
2.7%
691011
 
2.7%
65983
 
2.6%
66962
 
2.6%
75942
 
2.5%
Other values (878)27370
72.9%

Most occurring characters

ValueCountFrequency (%)
614141
17.3%
714058
17.2%
510073
12.3%
87679
9.4%
36960
8.5%
46918
8.5%
25454
 
6.7%
15141
 
6.3%
94311
 
5.3%
03817
 
4.7%
Other values (2)3281
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number78552
96.0%
Math Symbol1872
 
2.3%
Dash Punctuation1409
 
1.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
614141
18.0%
714058
17.9%
510073
12.8%
87679
9.8%
36960
8.9%
46918
8.8%
25454
 
6.9%
15141
 
6.5%
94311
 
5.5%
03817
 
4.9%
Dash Punctuation
ValueCountFrequency (%)
-1409
100.0%
Math Symbol
ValueCountFrequency (%)
+1872
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common81833
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
614141
17.3%
714058
17.2%
510073
12.3%
87679
9.4%
36960
8.5%
46918
8.5%
25454
 
6.7%
15141
 
6.3%
94311
 
5.3%
03817
 
4.7%
Other values (2)3281
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII81833
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
614141
17.3%
714058
17.2%
510073
12.3%
87679
9.4%
36960
8.5%
46918
8.5%
25454
 
6.7%
15141
 
6.3%
94311
 
5.3%
03817
 
4.7%
Other values (2)3281
 
4.0%

movement_reactions
Categorical

HIGH CARDINALITY

Distinct673
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
64
 
1637
62
 
1627
65
 
1622
60
 
1504
63
 
1496
Other values (668)
29662 

Length

Max length5
Median length2
Mean length2.119313945
Min length2

Characters and Unicode

Total characters79576
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique247 ?
Unique (%)0.7%

Sample

1st row75
2nd row67-2
3rd row70
4th row60
5th row59

Common Values

ValueCountFrequency (%)
641637
 
4.4%
621627
 
4.3%
651622
 
4.3%
601504
 
4.0%
631496
 
4.0%
581429
 
3.8%
681396
 
3.7%
661381
 
3.7%
591315
 
3.5%
611309
 
3.5%
Other values (663)22832
60.8%

Length

2022-01-13T14:14:12.262521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
641637
 
4.4%
621627
 
4.3%
651622
 
4.3%
601504
 
4.0%
631496
 
4.0%
581429
 
3.8%
681396
 
3.7%
661381
 
3.7%
591315
 
3.5%
611309
 
3.5%
Other values (663)22832
60.8%

Most occurring characters

ValueCountFrequency (%)
618988
23.9%
515498
19.5%
710367
13.0%
47219
 
9.1%
84986
 
6.3%
24588
 
5.8%
34351
 
5.5%
14107
 
5.2%
03851
 
4.8%
93456
 
4.3%
Other values (2)2165
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number77411
97.3%
Math Symbol1483
 
1.9%
Dash Punctuation682
 
0.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
618988
24.5%
515498
20.0%
710367
13.4%
47219
 
9.3%
84986
 
6.4%
24588
 
5.9%
34351
 
5.6%
14107
 
5.3%
03851
 
5.0%
93456
 
4.5%
Dash Punctuation
ValueCountFrequency (%)
-682
100.0%
Math Symbol
ValueCountFrequency (%)
+1483
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common79576
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
618988
23.9%
515498
19.5%
710367
13.0%
47219
 
9.1%
84986
 
6.3%
24588
 
5.8%
34351
 
5.5%
14107
 
5.2%
03851
 
4.8%
93456
 
4.3%
Other values (2)2165
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII79576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
618988
23.9%
515498
19.5%
710367
13.0%
47219
 
9.1%
84986
 
6.3%
24588
 
5.8%
34351
 
5.5%
14107
 
5.2%
03851
 
4.8%
93456
 
4.3%
Other values (2)2165
 
2.7%

movement_balance
Categorical

HIGH CARDINALITY

Distinct796
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
68
 
1203
65
 
1147
72
 
1132
66
 
1119
70
 
1106
Other values (791)
31841 

Length

Max length5
Median length2
Mean length2.124853521
Min length2

Characters and Unicode

Total characters79784
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique360 ?
Unique (%)1.0%

Sample

1st row76
2nd row65
3rd row88
4th row56
5th row59

Common Values

ValueCountFrequency (%)
681203
 
3.2%
651147
 
3.1%
721132
 
3.0%
661119
 
3.0%
701106
 
2.9%
671105
 
2.9%
691099
 
2.9%
711082
 
2.9%
641051
 
2.8%
751007
 
2.7%
Other values (786)26497
70.6%

Length

2022-01-13T14:14:12.380390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
681203
 
3.2%
651147
 
3.1%
721132
 
3.0%
661119
 
3.0%
701106
 
2.9%
671105
 
2.9%
691099
 
2.9%
711082
 
2.9%
641051
 
2.8%
751007
 
2.7%
Other values (786)26497
70.6%

Most occurring characters

ValueCountFrequency (%)
614657
18.4%
713870
17.4%
510441
13.1%
87457
9.3%
47240
9.1%
35949
7.5%
25025
 
6.3%
14846
 
6.1%
94223
 
5.3%
03795
 
4.8%
Other values (2)2281
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number77503
97.1%
Dash Punctuation1168
 
1.5%
Math Symbol1113
 
1.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
614657
18.9%
713870
17.9%
510441
13.5%
87457
9.6%
47240
9.3%
35949
7.7%
25025
 
6.5%
14846
 
6.3%
94223
 
5.4%
03795
 
4.9%
Dash Punctuation
ValueCountFrequency (%)
-1168
100.0%
Math Symbol
ValueCountFrequency (%)
+1113
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common79784
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
614657
18.4%
713870
17.4%
510441
13.1%
87457
9.3%
47240
9.1%
35949
7.5%
25025
 
6.3%
14846
 
6.1%
94223
 
5.3%
03795
 
4.8%
Other values (2)2281
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII79784
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
614657
18.4%
713870
17.4%
510441
13.1%
87457
9.3%
47240
9.1%
35949
7.5%
25025
 
6.3%
14846
 
6.1%
94223
 
5.3%
03795
 
4.8%
Other values (2)2281
 
2.9%

power_shot_power
Categorical

HIGH CARDINALITY

Distinct671
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
68
 
1116
70
 
1081
65
 
1052
62
 
1028
64
 
999
Other values (666)
32272 

Length

Max length5
Median length2
Mean length2.069564291
Min length1

Characters and Unicode

Total characters77708
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique331 ?
Unique (%)0.9%

Sample

1st row88
2nd row74
3rd row75
4th row70
5th row44

Common Values

ValueCountFrequency (%)
681116
 
3.0%
701081
 
2.9%
651052
 
2.8%
621028
 
2.7%
64999
 
2.7%
58996
 
2.7%
66989
 
2.6%
60973
 
2.6%
59957
 
2.5%
63944
 
2.5%
Other values (661)27413
73.0%

Length

2022-01-13T14:14:12.476536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
681116
 
3.0%
701081
 
2.9%
651052
 
2.8%
621028
 
2.7%
64999
 
2.7%
58996
 
2.7%
66989
 
2.6%
60973
 
2.6%
59957
 
2.5%
63944
 
2.5%
Other values (661)27413
73.0%

Most occurring characters

ValueCountFrequency (%)
613736
17.7%
512233
15.7%
710485
13.5%
49247
11.9%
26723
8.7%
36311
8.1%
85408
 
7.0%
14761
 
6.1%
04055
 
5.2%
93513
 
4.5%
Other values (2)1236
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number76472
98.4%
Math Symbol819
 
1.1%
Dash Punctuation417
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
613736
18.0%
512233
16.0%
710485
13.7%
49247
12.1%
26723
8.8%
36311
8.3%
85408
 
7.1%
14761
 
6.2%
04055
 
5.3%
93513
 
4.6%
Math Symbol
ValueCountFrequency (%)
+819
100.0%
Dash Punctuation
ValueCountFrequency (%)
-417
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common77708
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
613736
17.7%
512233
15.7%
710485
13.5%
49247
11.9%
26723
8.7%
36311
8.1%
85408
 
7.0%
14761
 
6.1%
04055
 
5.2%
93513
 
4.5%
Other values (2)1236
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII77708
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
613736
17.7%
512233
15.7%
710485
13.5%
49247
11.9%
26723
8.7%
36311
8.1%
85408
 
7.0%
14761
 
6.1%
04055
 
5.2%
93513
 
4.5%
Other values (2)1236
 
1.6%

power_jumping
Categorical

HIGH CARDINALITY

Distinct850
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
70
 
1332
72
 
1268
71
 
1259
67
 
1193
63
 
1189
Other values (845)
31307 

Length

Max length5
Median length2
Mean length2.16855758
Min length2

Characters and Unicode

Total characters81425
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique379 ?
Unique (%)1.0%

Sample

1st row65
2nd row68
3rd row84
4th row82
5th row52

Common Values

ValueCountFrequency (%)
701332
 
3.5%
721268
 
3.4%
711259
 
3.4%
671193
 
3.2%
631189
 
3.2%
651182
 
3.1%
621172
 
3.1%
681171
 
3.1%
661146
 
3.1%
641134
 
3.0%
Other values (840)25502
67.9%

Length

2022-01-13T14:14:12.579923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
701332
 
3.5%
721268
 
3.4%
711259
 
3.4%
671193
 
3.2%
631189
 
3.2%
651182
 
3.1%
621172
 
3.1%
681171
 
3.1%
661146
 
3.1%
641134
 
3.0%
Other values (840)25502
67.9%

Most occurring characters

ValueCountFrequency (%)
615829
19.4%
713972
17.2%
511450
14.1%
87016
8.6%
46330
 
7.8%
35564
 
6.8%
15130
 
6.3%
25085
 
6.2%
04185
 
5.1%
93792
 
4.7%
Other values (2)3072
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number78353
96.2%
Math Symbol2053
 
2.5%
Dash Punctuation1019
 
1.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
615829
20.2%
713972
17.8%
511450
14.6%
87016
9.0%
46330
 
8.1%
35564
 
7.1%
15130
 
6.5%
25085
 
6.5%
04185
 
5.3%
93792
 
4.8%
Math Symbol
ValueCountFrequency (%)
+2053
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1019
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common81425
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
615829
19.4%
713972
17.2%
511450
14.1%
87016
8.6%
46330
 
7.8%
35564
 
6.8%
15130
 
6.3%
25085
 
6.2%
04185
 
5.1%
93792
 
4.7%
Other values (2)3072
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII81425
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
615829
19.4%
713972
17.2%
511450
14.1%
87016
8.6%
46330
 
7.8%
35564
 
6.8%
15130
 
6.3%
25085
 
6.2%
04185
 
5.1%
93792
 
4.7%
Other values (2)3072
 
3.8%

power_stamina
Categorical

HIGH CARDINALITY

Distinct1100
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
68
 
1162
67
 
1109
69
 
1099
66
 
1098
72
 
1082
Other values (1095)
31998 

Length

Max length5
Median length2
Mean length2.224938745
Min length2

Characters and Unicode

Total characters83542
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique479 ?
Unique (%)1.3%

Sample

1st row73
2nd row82
3rd row68
4th row62
5th row66

Common Values

ValueCountFrequency (%)
681162
 
3.1%
671109
 
3.0%
691099
 
2.9%
661098
 
2.9%
721082
 
2.9%
701054
 
2.8%
651054
 
2.8%
731049
 
2.8%
641006
 
2.7%
71998
 
2.7%
Other values (1090)26837
71.5%

Length

2022-01-13T14:14:12.685582image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
681162
 
3.1%
671109
 
3.0%
691099
 
2.9%
661098
 
2.9%
721082
 
2.9%
701054
 
2.8%
651054
 
2.8%
731049
 
2.8%
641006
 
2.7%
71998
 
2.7%
Other values (1090)26837
71.5%

Most occurring characters

ValueCountFrequency (%)
614924
17.9%
714170
17.0%
59686
11.6%
87546
9.0%
36668
8.0%
26541
7.8%
46266
7.5%
15587
 
6.7%
94177
 
5.0%
03957
 
4.7%
Other values (2)4020
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number79522
95.2%
Math Symbol2195
 
2.6%
Dash Punctuation1825
 
2.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
614924
18.8%
714170
17.8%
59686
12.2%
87546
9.5%
36668
8.4%
26541
8.2%
46266
7.9%
15587
 
7.0%
94177
 
5.3%
03957
 
5.0%
Dash Punctuation
ValueCountFrequency (%)
-1825
100.0%
Math Symbol
ValueCountFrequency (%)
+2195
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common83542
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
614924
17.9%
714170
17.0%
59686
11.6%
87546
9.0%
36668
8.0%
26541
7.8%
46266
7.5%
15587
 
6.7%
94177
 
5.0%
03957
 
4.7%
Other values (2)4020
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII83542
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
614924
17.9%
714170
17.0%
59686
11.6%
87546
9.0%
36668
8.0%
26541
7.8%
46266
7.5%
15587
 
6.7%
94177
 
5.0%
03957
 
4.7%
Other values (2)4020
 
4.8%

power_strength
Categorical

HIGH CARDINALITY

Distinct927
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
68
 
1148
70
 
1136
72
 
1121
64
 
1099
67
 
1097
Other values (922)
31947 

Length

Max length5
Median length2
Mean length2.198599126
Min length2

Characters and Unicode

Total characters82553
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique401 ?
Unique (%)1.1%

Sample

1st row69-2
2nd row65
3rd row63
4th row81
5th row68

Common Values

ValueCountFrequency (%)
681148
 
3.1%
701136
 
3.0%
721121
 
3.0%
641099
 
2.9%
671097
 
2.9%
691091
 
2.9%
651079
 
2.9%
711072
 
2.9%
621066
 
2.8%
731051
 
2.8%
Other values (917)26588
70.8%

Length

2022-01-13T14:14:12.853755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
681148
 
3.1%
701136
 
3.0%
721121
 
3.0%
641099
 
2.9%
671097
 
2.9%
691091
 
2.9%
651079
 
2.9%
711072
 
2.9%
621066
 
2.8%
731051
 
2.8%
Other values (917)26588
70.8%

Most occurring characters

ValueCountFrequency (%)
615384
18.6%
714626
17.7%
510494
12.7%
87553
9.1%
46963
8.4%
35503
 
6.7%
15100
 
6.2%
24917
 
6.0%
94415
 
5.3%
03974
 
4.8%
Other values (2)3624
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number78929
95.6%
Math Symbol2694
 
3.3%
Dash Punctuation930
 
1.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
615384
19.5%
714626
18.5%
510494
13.3%
87553
9.6%
46963
8.8%
35503
 
7.0%
15100
 
6.5%
24917
 
6.2%
94415
 
5.6%
03974
 
5.0%
Dash Punctuation
ValueCountFrequency (%)
-930
100.0%
Math Symbol
ValueCountFrequency (%)
+2694
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common82553
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
615384
18.6%
714626
17.7%
510494
12.7%
87553
9.1%
46963
8.4%
35503
 
6.7%
15100
 
6.2%
24917
 
6.0%
94415
 
5.3%
03974
 
4.8%
Other values (2)3624
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII82553
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
615384
18.6%
714626
17.7%
510494
12.7%
87553
9.1%
46963
8.4%
35503
 
6.7%
15100
 
6.2%
24917
 
6.0%
94415
 
5.3%
03974
 
4.8%
Other values (2)3624
 
4.4%

power_long_shots
Categorical

HIGH CARDINALITY

Distinct777
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
58
 
948
59
 
925
62
 
898
55
 
870
60
 
829
Other values (772)
33078 

Length

Max length5
Median length2
Mean length2.046766805
Min length1

Characters and Unicode

Total characters76852
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique427 ?
Unique (%)1.1%

Sample

1st row88
2nd row63
3rd row73
4th row65
5th row28

Common Values

ValueCountFrequency (%)
58948
 
2.5%
59925
 
2.5%
62898
 
2.4%
55870
 
2.3%
60829
 
2.2%
56807
 
2.1%
64803
 
2.1%
52795
 
2.1%
65792
 
2.1%
66778
 
2.1%
Other values (767)29103
77.5%

Length

2022-01-13T14:14:12.992993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
58948
 
2.5%
59925
 
2.5%
62898
 
2.4%
55870
 
2.3%
60829
 
2.2%
56807
 
2.1%
64803
 
2.1%
52795
 
2.1%
65792
 
2.1%
66778
 
2.1%
Other values (767)29103
77.5%

Most occurring characters

ValueCountFrequency (%)
512085
15.7%
611892
15.5%
49124
11.9%
28058
10.5%
37949
10.3%
17498
9.8%
77028
9.1%
84495
 
5.8%
93764
 
4.9%
03618
 
4.7%
Other values (2)1341
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number75511
98.3%
Math Symbol885
 
1.2%
Dash Punctuation456
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
512085
16.0%
611892
15.7%
49124
12.1%
28058
10.7%
37949
10.5%
17498
9.9%
77028
9.3%
84495
 
6.0%
93764
 
5.0%
03618
 
4.8%
Math Symbol
ValueCountFrequency (%)
+885
100.0%
Dash Punctuation
ValueCountFrequency (%)
-456
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common76852
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
512085
15.7%
611892
15.5%
49124
11.9%
28058
10.5%
37949
10.3%
17498
9.8%
77028
9.1%
84495
 
5.8%
93764
 
4.9%
03618
 
4.7%
Other values (2)1341
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII76852
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
512085
15.7%
611892
15.5%
49124
11.9%
28058
10.5%
37949
10.3%
17498
9.8%
77028
9.1%
84495
 
5.8%
93764
 
4.9%
03618
 
4.7%
Other values (2)1341
 
1.7%

mentality_aggression
Categorical

HIGH CARDINALITY

Distinct760
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
70
 
996
68
 
969
65
 
942
60
 
917
58
 
886
Other values (755)
32838 

Length

Max length5
Median length2
Mean length2.072866731
Min length2

Characters and Unicode

Total characters77832
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique413 ?
Unique (%)1.1%

Sample

1st row70
2nd row72-4
3rd row73
4th row52
5th row55

Common Values

ValueCountFrequency (%)
70996
 
2.7%
68969
 
2.6%
65942
 
2.5%
60917
 
2.4%
58886
 
2.4%
59850
 
2.3%
55835
 
2.2%
66828
 
2.2%
67825
 
2.2%
62822
 
2.2%
Other values (750)28678
76.4%

Length

2022-01-13T14:14:13.111086image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
70996
 
2.7%
68969
 
2.6%
65942
 
2.5%
60917
 
2.4%
58886
 
2.4%
59850
 
2.3%
55835
 
2.2%
66828
 
2.2%
67825
 
2.2%
62822
 
2.2%
Other values (750)28678
76.4%

Most occurring characters

ValueCountFrequency (%)
612329
15.8%
511401
14.6%
710385
13.3%
48607
11.1%
37618
9.8%
27070
9.1%
86450
8.3%
14750
 
6.1%
04189
 
5.4%
93757
 
4.8%
Other values (2)1276
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number76556
98.4%
Math Symbol879
 
1.1%
Dash Punctuation397
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
612329
16.1%
511401
14.9%
710385
13.6%
48607
11.2%
37618
10.0%
27070
9.2%
86450
8.4%
14750
 
6.2%
04189
 
5.5%
93757
 
4.9%
Dash Punctuation
ValueCountFrequency (%)
-397
100.0%
Math Symbol
ValueCountFrequency (%)
+879
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common77832
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
612329
15.8%
511401
14.6%
710385
13.3%
48607
11.1%
37618
9.8%
27070
9.1%
86450
8.3%
14750
 
6.1%
04189
 
5.4%
93757
 
4.8%
Other values (2)1276
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII77832
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
612329
15.8%
511401
14.6%
710385
13.3%
48607
11.1%
37618
9.8%
27070
9.1%
86450
8.3%
14750
 
6.1%
04189
 
5.4%
93757
 
4.8%
Other values (2)1276
 
1.6%

mentality_interceptions
Categorical

HIGH CARDINALITY

Distinct761
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
65
 
977
62
 
959
63
 
899
64
 
878
66
 
872
Other values (756)
32963 

Length

Max length5
Median length2
Mean length2.076515394
Min length1

Characters and Unicode

Total characters77969
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique391 ?
Unique (%)1.0%

Sample

1st row71
2nd row70-5
3rd row42
4th row20
5th row58

Common Values

ValueCountFrequency (%)
65977
 
2.6%
62959
 
2.6%
63899
 
2.4%
64878
 
2.3%
66872
 
2.3%
23809
 
2.2%
22803
 
2.1%
60793
 
2.1%
59769
 
2.0%
67764
 
2.0%
Other values (751)29025
77.3%

Length

2022-01-13T14:14:13.218249image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
65977
 
2.6%
62959
 
2.6%
63899
 
2.4%
64878
 
2.3%
66872
 
2.3%
23809
 
2.2%
22803
 
2.1%
60793
 
2.1%
59769
 
2.0%
67764
 
2.0%
Other values (751)29025
77.3%

Most occurring characters

ValueCountFrequency (%)
612575
16.1%
210787
13.8%
510441
13.4%
18660
11.1%
77572
9.7%
47384
9.5%
37209
9.2%
84454
 
5.7%
03699
 
4.7%
93611
 
4.6%
Other values (2)1577
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number76392
98.0%
Math Symbol1131
 
1.5%
Dash Punctuation446
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
612575
16.5%
210787
14.1%
510441
13.7%
18660
11.3%
77572
9.9%
47384
9.7%
37209
9.4%
84454
 
5.8%
03699
 
4.8%
93611
 
4.7%
Dash Punctuation
ValueCountFrequency (%)
-446
100.0%
Math Symbol
ValueCountFrequency (%)
+1131
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common77969
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
612575
16.1%
210787
13.8%
510441
13.4%
18660
11.1%
77572
9.7%
47384
9.5%
37209
9.2%
84454
 
5.7%
03699
 
4.7%
93611
 
4.6%
Other values (2)1577
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII77969
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
612575
16.1%
210787
13.8%
510441
13.4%
18660
11.1%
77572
9.7%
47384
9.5%
37209
9.2%
84454
 
5.7%
03699
 
4.7%
93611
 
4.6%
Other values (2)1577
 
2.0%

mentality_positioning
Categorical

HIGH CARDINALITY

Distinct721
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
58
 
1064
62
 
1059
65
 
1030
60
 
999
59
 
938
Other values (716)
32458 

Length

Max length5
Median length2
Mean length2.048284862
Min length1

Characters and Unicode

Total characters76909
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique330 ?
Unique (%)0.9%

Sample

1st row74
2nd row57
3rd row70
4th row73
5th row39

Common Values

ValueCountFrequency (%)
581064
 
2.8%
621059
 
2.8%
651030
 
2.7%
60999
 
2.7%
59938
 
2.5%
64934
 
2.5%
55928
 
2.5%
68891
 
2.4%
57887
 
2.4%
63879
 
2.3%
Other values (711)27939
74.4%

Length

2022-01-13T14:14:13.366421image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
581064
 
2.8%
621059
 
2.8%
651030
 
2.7%
60999
 
2.7%
59938
 
2.5%
64934
 
2.5%
55928
 
2.5%
68891
 
2.4%
57887
 
2.4%
63879
 
2.3%
Other values (711)27939
74.4%

Most occurring characters

ValueCountFrequency (%)
613282
17.3%
512970
16.9%
48693
11.3%
77792
10.1%
27186
9.3%
16780
8.8%
36613
8.6%
84764
 
6.2%
03710
 
4.8%
93591
 
4.7%
Other values (2)1528
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number75381
98.0%
Math Symbol1042
 
1.4%
Dash Punctuation486
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
613282
17.6%
512970
17.2%
48693
11.5%
77792
10.3%
27186
9.5%
16780
9.0%
36613
8.8%
84764
 
6.3%
03710
 
4.9%
93591
 
4.8%
Dash Punctuation
ValueCountFrequency (%)
-486
100.0%
Math Symbol
ValueCountFrequency (%)
+1042
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common76909
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
613282
17.3%
512970
16.9%
48693
11.3%
77792
10.1%
27186
9.3%
16780
8.8%
36613
8.6%
84764
 
6.2%
03710
 
4.8%
93591
 
4.7%
Other values (2)1528
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII76909
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
613282
17.3%
512970
16.9%
48693
11.3%
77792
10.1%
27186
9.3%
16780
8.8%
36613
8.6%
84764
 
6.2%
03710
 
4.8%
93591
 
4.7%
Other values (2)1528
 
2.0%

mentality_vision
Categorical

HIGH CARDINALITY

Distinct718
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
58
 
1132
55
 
1061
59
 
1045
65
 
1035
60
 
1033
Other values (713)
32242 

Length

Max length5
Median length2
Mean length2.087701076
Min length1

Characters and Unicode

Total characters78389
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique334 ?
Unique (%)0.9%

Sample

1st row83
2nd row72
3rd row73
4th row55
5th row47

Common Values

ValueCountFrequency (%)
581132
 
3.0%
551061
 
2.8%
591045
 
2.8%
651035
 
2.8%
601033
 
2.8%
62986
 
2.6%
64977
 
2.6%
63938
 
2.5%
52929
 
2.5%
54914
 
2.4%
Other values (708)27498
73.2%

Length

2022-01-13T14:14:13.472968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
581132
 
3.0%
551061
 
2.8%
591045
 
2.8%
651035
 
2.8%
601033
 
2.8%
62986
 
2.6%
64977
 
2.6%
63938
 
2.5%
52929
 
2.5%
54914
 
2.4%
Other values (708)27498
73.2%

Most occurring characters

ValueCountFrequency (%)
514197
18.1%
612961
16.5%
410668
13.6%
38737
11.1%
77448
9.5%
26166
7.9%
84547
 
5.8%
14444
 
5.7%
03861
 
4.9%
93792
 
4.8%
Other values (2)1568
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number76821
98.0%
Math Symbol1166
 
1.5%
Dash Punctuation402
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
514197
18.5%
612961
16.9%
410668
13.9%
38737
11.4%
77448
9.7%
26166
8.0%
84547
 
5.9%
14444
 
5.8%
03861
 
5.0%
93792
 
4.9%
Math Symbol
ValueCountFrequency (%)
+1166
100.0%
Dash Punctuation
ValueCountFrequency (%)
-402
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common78389
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
514197
18.1%
612961
16.5%
410668
13.6%
38737
11.1%
77448
9.5%
26166
7.9%
84547
 
5.8%
14444
 
5.7%
03861
 
4.9%
93792
 
4.8%
Other values (2)1568
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII78389
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
514197
18.1%
612961
16.5%
410668
13.6%
38737
11.1%
77448
9.5%
26166
7.9%
84547
 
5.8%
14444
 
5.7%
03861
 
4.9%
93792
 
4.8%
Other values (2)1568
 
2.0%

mentality_penalties
Categorical

HIGH CARDINALITY

Distinct531
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
55
 
1043
48
 
978
58
 
959
59
 
957
49
 
945
Other values (526)
32666 

Length

Max length5
Median length2
Mean length2.034116331
Min length1

Characters and Unicode

Total characters76377
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique334 ?
Unique (%)0.9%

Sample

1st row76
2nd row55
3rd row67
4th row72
5th row40

Common Values

ValueCountFrequency (%)
551043
 
2.8%
48978
 
2.6%
58959
 
2.6%
59957
 
2.5%
49945
 
2.5%
60914
 
2.4%
45911
 
2.4%
40871
 
2.3%
42870
 
2.3%
50867
 
2.3%
Other values (521)28233
75.2%

Length

2022-01-13T14:14:13.584977image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
551043
 
2.8%
48978
 
2.6%
58959
 
2.6%
59957
 
2.5%
49945
 
2.5%
60914
 
2.4%
45911
 
2.4%
40871
 
2.3%
42870
 
2.3%
50867
 
2.3%
Other values (521)28233
75.2%

Most occurring characters

ValueCountFrequency (%)
512997
17.0%
412417
16.3%
611088
14.5%
38964
11.7%
26426
8.4%
16061
7.9%
75930
7.8%
84193
 
5.5%
03998
 
5.2%
93704
 
4.8%
Other values (2)599
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number75778
99.2%
Math Symbol335
 
0.4%
Dash Punctuation264
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
512997
17.2%
412417
16.4%
611088
14.6%
38964
11.8%
26426
8.5%
16061
8.0%
75930
7.8%
84193
 
5.5%
03998
 
5.3%
93704
 
4.9%
Math Symbol
ValueCountFrequency (%)
+335
100.0%
Dash Punctuation
ValueCountFrequency (%)
-264
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common76377
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
512997
17.0%
412417
16.3%
611088
14.5%
38964
11.7%
26426
8.4%
16061
7.9%
75930
7.8%
84193
 
5.5%
03998
 
5.2%
93704
 
4.8%
Other values (2)599
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII76377
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
512997
17.0%
412417
16.3%
611088
14.5%
38964
11.7%
26426
8.4%
16061
7.9%
75930
7.8%
84193
 
5.5%
03998
 
5.2%
93704
 
4.8%
Other values (2)599
 
0.8%

mentality_composure
Categorical

HIGH CARDINALITY

Distinct1018
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
60
 
1357
65
 
1303
55
 
1302
58
 
1294
62
 
1214
Other values (1013)
31078 

Length

Max length5
Median length2
Mean length2.185495899
Min length1

Characters and Unicode

Total characters82061
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique362 ?
Unique (%)1.0%

Sample

1st row81
2nd row68-2
3rd row71
4th row66
5th row40

Common Values

ValueCountFrequency (%)
601357
 
3.6%
651303
 
3.5%
551302
 
3.5%
581294
 
3.4%
621214
 
3.2%
641181
 
3.1%
591137
 
3.0%
631083
 
2.9%
671066
 
2.8%
661048
 
2.8%
Other values (1008)25563
68.1%

Length

2022-01-13T14:14:13.696640image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
601357
 
3.6%
651303
 
3.5%
551302
 
3.5%
581294
 
3.4%
621214
 
3.2%
641181
 
3.1%
591137
 
3.0%
631083
 
2.9%
671066
 
2.8%
661048
 
2.8%
Other values (1008)25563
68.1%

Most occurring characters

ValueCountFrequency (%)
616199
19.7%
515190
18.5%
79401
11.5%
49178
11.2%
35699
 
6.9%
25418
 
6.6%
85076
 
6.2%
14661
 
5.7%
04517
 
5.5%
93769
 
4.6%
Other values (2)2953
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number79108
96.4%
Math Symbol2551
 
3.1%
Dash Punctuation402
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
616199
20.5%
515190
19.2%
79401
11.9%
49178
11.6%
35699
 
7.2%
25418
 
6.8%
85076
 
6.4%
14661
 
5.9%
04517
 
5.7%
93769
 
4.8%
Dash Punctuation
ValueCountFrequency (%)
-402
100.0%
Math Symbol
ValueCountFrequency (%)
+2551
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common82061
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
616199
19.7%
515190
18.5%
79401
11.5%
49178
11.2%
35699
 
6.9%
25418
 
6.6%
85076
 
6.2%
14661
 
5.7%
04517
 
5.5%
93769
 
4.6%
Other values (2)2953
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII82061
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
616199
19.7%
515190
18.5%
79401
11.5%
49178
11.2%
35699
 
6.9%
25418
 
6.6%
85076
 
6.2%
14661
 
5.7%
04517
 
5.5%
93769
 
4.6%
Other values (2)2953
 
3.6%

defending_marking
Categorical

HIGH CARDINALITY

Distinct804
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
60
 
977
65
 
971
62
 
950
64
 
919
66
 
864
Other values (799)
32867 

Length

Max length5
Median length2
Mean length2.076755087
Min length1

Characters and Unicode

Total characters77978
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique412 ?
Unique (%)1.1%

Sample

1st row66
2nd row69
3rd row50
4th row45
5th row58

Common Values

ValueCountFrequency (%)
60977
 
2.6%
65971
 
2.6%
62950
 
2.5%
64919
 
2.4%
66864
 
2.3%
58845
 
2.3%
63828
 
2.2%
68781
 
2.1%
59777
 
2.1%
55729
 
1.9%
Other values (794)28907
77.0%

Length

2022-01-13T14:14:13.804676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
60977
 
2.6%
65971
 
2.6%
62950
 
2.5%
64919
 
2.4%
66864
 
2.3%
58845
 
2.3%
63828
 
2.2%
68781
 
2.1%
59777
 
2.1%
55729
 
1.9%
Other values (794)28907
77.0%

Most occurring characters

ValueCountFrequency (%)
612506
16.0%
510671
13.7%
29343
12.0%
18828
11.3%
37681
9.9%
47463
9.6%
77232
9.3%
84461
 
5.7%
04394
 
5.6%
93637
 
4.7%
Other values (2)1762
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number76216
97.7%
Math Symbol1139
 
1.5%
Dash Punctuation623
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
612506
16.4%
510671
14.0%
29343
12.3%
18828
11.6%
37681
10.1%
47463
9.8%
77232
9.5%
84461
 
5.9%
04394
 
5.8%
93637
 
4.8%
Math Symbol
ValueCountFrequency (%)
+1139
100.0%
Dash Punctuation
ValueCountFrequency (%)
-623
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common77978
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
612506
16.0%
510671
13.7%
29343
12.0%
18828
11.3%
37681
9.9%
47463
9.6%
77232
9.3%
84461
 
5.7%
04394
 
5.6%
93637
 
4.7%
Other values (2)1762
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII77978
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
612506
16.0%
510671
13.7%
29343
12.0%
18828
11.3%
37681
9.9%
47463
9.6%
77232
9.3%
84461
 
5.7%
04394
 
5.6%
93637
 
4.7%
Other values (2)1762
 
2.3%

defending_standing_tackle
Categorical

HIGH CARDINALITY

Distinct764
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
66
 
1101
64
 
1093
65
 
1087
62
 
949
67
 
937
Other values (759)
32381 

Length

Max length5
Median length2
Mean length2.099286247
Min length1

Characters and Unicode

Total characters78824
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique397 ?
Unique (%)1.1%

Sample

1st row72
2nd row72-1
3rd row53
4th row18
5th row56

Common Values

ValueCountFrequency (%)
661101
 
2.9%
641093
 
2.9%
651087
 
2.9%
62949
 
2.5%
67937
 
2.5%
68936
 
2.5%
63933
 
2.5%
13820
 
2.2%
14818
 
2.2%
70791
 
2.1%
Other values (754)28083
74.8%

Length

2022-01-13T14:14:13.916216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
661101
 
2.9%
641093
 
2.9%
651087
 
2.9%
62949
 
2.5%
67937
 
2.5%
68936
 
2.5%
63933
 
2.5%
13820
 
2.2%
14818
 
2.2%
70791
 
2.1%
Other values (754)28083
74.8%

Most occurring characters

ValueCountFrequency (%)
613547
17.2%
110452
13.3%
29052
11.5%
78776
11.1%
58617
10.9%
37852
10.0%
47234
9.2%
84336
 
5.5%
03728
 
4.7%
93368
 
4.3%
Other values (2)1862
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number76962
97.6%
Math Symbol1194
 
1.5%
Dash Punctuation668
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
613547
17.6%
110452
13.6%
29052
11.8%
78776
11.4%
58617
11.2%
37852
10.2%
47234
9.4%
84336
 
5.6%
03728
 
4.8%
93368
 
4.4%
Dash Punctuation
ValueCountFrequency (%)
-668
100.0%
Math Symbol
ValueCountFrequency (%)
+1194
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common78824
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
613547
17.2%
110452
13.3%
29052
11.5%
78776
11.1%
58617
10.9%
37852
10.0%
47234
9.2%
84336
 
5.5%
03728
 
4.7%
93368
 
4.3%
Other values (2)1862
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII78824
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
613547
17.2%
110452
13.3%
29052
11.5%
78776
11.1%
58617
10.9%
37852
10.0%
47234
9.2%
84336
 
5.5%
03728
 
4.7%
93368
 
4.3%
Other values (2)1862
 
2.4%

defending_sliding_tackle
Categorical

HIGH CARDINALITY

Distinct727
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
62
 
1066
64
 
1024
65
 
996
63
 
985
13
 
913
Other values (722)
32564 

Length

Max length5
Median length2
Mean length2.088020667
Min length1

Characters and Unicode

Total characters78401
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique367 ?
Unique (%)1.0%

Sample

1st row69
2nd row68-2
3rd row31
4th row13
5th row57

Common Values

ValueCountFrequency (%)
621066
 
2.8%
641024
 
2.7%
65996
 
2.7%
63985
 
2.6%
13913
 
2.4%
12892
 
2.4%
60888
 
2.4%
14855
 
2.3%
66852
 
2.3%
68838
 
2.2%
Other values (717)28239
75.2%

Length

2022-01-13T14:14:14.025852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
621066
 
2.8%
641024
 
2.7%
65996
 
2.7%
63985
 
2.6%
13913
 
2.4%
12892
 
2.4%
60888
 
2.4%
14855
 
2.3%
66852
 
2.3%
68838
 
2.2%
Other values (717)28239
75.2%

Most occurring characters

ValueCountFrequency (%)
613052
16.6%
111146
14.2%
59666
12.3%
29567
12.2%
37518
9.6%
77290
9.3%
47249
9.2%
84080
 
5.2%
03796
 
4.8%
93380
 
4.3%
Other values (2)1657
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number76744
97.9%
Math Symbol1048
 
1.3%
Dash Punctuation609
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
613052
17.0%
111146
14.5%
59666
12.6%
29567
12.5%
37518
9.8%
77290
9.5%
47249
9.4%
84080
 
5.3%
03796
 
4.9%
93380
 
4.4%
Dash Punctuation
ValueCountFrequency (%)
-609
100.0%
Math Symbol
ValueCountFrequency (%)
+1048
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common78401
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
613052
16.6%
111146
14.2%
59666
12.3%
29567
12.2%
37518
9.6%
77290
9.3%
47249
9.2%
84080
 
5.2%
03796
 
4.8%
93380
 
4.3%
Other values (2)1657
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII78401
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
613052
16.6%
111146
14.2%
59666
12.3%
29567
12.2%
37518
9.6%
77290
9.3%
47249
9.2%
84080
 
5.2%
03796
 
4.8%
93380
 
4.3%
Other values (2)1657
 
2.1%

goalkeeping_diving
Categorical

HIGH CARDINALITY

Distinct266
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
8
3358 
9
3291 
12
3290 
7
3272 
10
3256 
Other values (261)
21081 

Length

Max length5
Median length2
Mean length1.657664856
Min length1

Characters and Unicode

Total characters62242
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique122 ?
Unique (%)0.3%

Sample

1st row6
2nd row8
3rd row6
4th row8
5th row10

Common Values

ValueCountFrequency (%)
83358
8.9%
93291
8.8%
123290
8.8%
73272
8.7%
103256
8.7%
143241
8.6%
133197
8.5%
113066
8.2%
62660
7.1%
152529
 
6.7%
Other values (256)6388
17.0%

Length

2022-01-13T14:14:14.142021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
83358
8.9%
93291
8.8%
123290
8.8%
73272
8.7%
103256
8.7%
143241
8.6%
133197
8.5%
113066
8.2%
62660
7.1%
152529
 
6.7%
Other values (256)6388
17.0%

Most occurring characters

ValueCountFrequency (%)
123441
37.7%
66290
 
10.1%
74746
 
7.6%
54664
 
7.5%
83973
 
6.4%
43883
 
6.2%
23837
 
6.2%
93723
 
6.0%
33714
 
6.0%
03636
 
5.8%
Other values (2)335
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number61907
99.5%
Math Symbol220
 
0.4%
Dash Punctuation115
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
123441
37.9%
66290
 
10.2%
74746
 
7.7%
54664
 
7.5%
83973
 
6.4%
43883
 
6.3%
23837
 
6.2%
93723
 
6.0%
33714
 
6.0%
03636
 
5.9%
Dash Punctuation
ValueCountFrequency (%)
-115
100.0%
Math Symbol
ValueCountFrequency (%)
+220
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common62242
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
123441
37.7%
66290
 
10.1%
74746
 
7.6%
54664
 
7.5%
83973
 
6.4%
43883
 
6.2%
23837
 
6.2%
93723
 
6.0%
33714
 
6.0%
03636
 
5.8%
Other values (2)335
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII62242
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
123441
37.7%
66290
 
10.1%
74746
 
7.6%
54664
 
7.5%
83973
 
6.4%
43883
 
6.2%
23837
 
6.2%
93723
 
6.0%
33714
 
6.0%
03636
 
5.8%
Other values (2)335
 
0.5%

goalkeeping_handling
Categorical

HIGH CARDINALITY

Distinct268
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
10
3396 
11
3332 
12
3294 
8
3272 
7
3254 
Other values (263)
21000 

Length

Max length5
Median length2
Mean length1.66855758
Min length1

Characters and Unicode

Total characters62651
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique126 ?
Unique (%)0.3%

Sample

1st row8
2nd row9
3rd row6
4th row11
5th row7

Common Values

ValueCountFrequency (%)
103396
9.0%
113332
8.9%
123294
8.8%
83272
8.7%
73254
8.7%
143217
8.6%
133158
8.4%
93108
8.3%
62600
6.9%
152456
 
6.5%
Other values (258)6461
17.2%

Length

2022-01-13T14:14:14.323473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
103396
9.0%
113332
8.9%
123294
8.8%
83272
8.7%
73254
8.7%
143217
8.6%
133158
8.4%
93108
8.3%
62600
6.9%
152456
 
6.5%
Other values (258)6461
17.2%

Most occurring characters

ValueCountFrequency (%)
124132
38.5%
66366
 
10.2%
54866
 
7.8%
74391
 
7.0%
23903
 
6.2%
03853
 
6.1%
43831
 
6.1%
83809
 
6.1%
33655
 
5.8%
93518
 
5.6%
Other values (2)327
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number62324
99.5%
Math Symbol207
 
0.3%
Dash Punctuation120
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
124132
38.7%
66366
 
10.2%
54866
 
7.8%
74391
 
7.0%
23903
 
6.3%
03853
 
6.2%
43831
 
6.1%
83809
 
6.1%
33655
 
5.9%
93518
 
5.6%
Dash Punctuation
ValueCountFrequency (%)
-120
100.0%
Math Symbol
ValueCountFrequency (%)
+207
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common62651
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
124132
38.5%
66366
 
10.2%
54866
 
7.8%
74391
 
7.0%
23903
 
6.2%
03853
 
6.1%
43831
 
6.1%
83809
 
6.1%
33655
 
5.8%
93518
 
5.6%
Other values (2)327
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII62651
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
124132
38.5%
66366
 
10.2%
54866
 
7.8%
74391
 
7.0%
23903
 
6.2%
03853
 
6.1%
43831
 
6.1%
83809
 
6.1%
33655
 
5.8%
93518
 
5.6%
Other values (2)327
 
0.5%

goalkeeping_kicking
Categorical

HIGH CARDINALITY

Distinct267
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
9
3435 
12
3386 
7
3314 
13
3283 
8
3201 
Other values (262)
20929 

Length

Max length5
Median length2
Mean length1.657931181
Min length1

Characters and Unicode

Total characters62252
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique134 ?
Unique (%)0.4%

Sample

1st row10
2nd row11
3rd row7
4th row9
5th row6

Common Values

ValueCountFrequency (%)
93435
9.1%
123386
9.0%
73314
8.8%
133283
8.7%
83201
8.5%
143194
8.5%
103167
8.4%
113059
8.1%
62472
 
6.6%
152462
 
6.6%
Other values (257)6575
17.5%

Length

2022-01-13T14:14:14.645707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
93435
9.1%
123386
9.0%
73314
8.8%
133283
8.7%
83201
8.5%
143194
8.5%
103167
8.4%
113059
8.1%
62472
 
6.6%
152462
 
6.6%
Other values (257)6575
17.5%

Most occurring characters

ValueCountFrequency (%)
123514
37.8%
66094
 
9.8%
55258
 
8.4%
74305
 
6.9%
43924
 
6.3%
23890
 
6.2%
93876
 
6.2%
33760
 
6.0%
83746
 
6.0%
03623
 
5.8%
Other values (2)262
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number61990
99.6%
Math Symbol167
 
0.3%
Dash Punctuation95
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
123514
37.9%
66094
 
9.8%
55258
 
8.5%
74305
 
6.9%
43924
 
6.3%
23890
 
6.3%
93876
 
6.3%
33760
 
6.1%
83746
 
6.0%
03623
 
5.8%
Dash Punctuation
ValueCountFrequency (%)
-95
100.0%
Math Symbol
ValueCountFrequency (%)
+167
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common62252
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
123514
37.8%
66094
 
9.8%
55258
 
8.4%
74305
 
6.9%
43924
 
6.3%
23890
 
6.2%
93876
 
6.2%
33760
 
6.0%
83746
 
6.0%
03623
 
5.8%
Other values (2)262
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII62252
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
123514
37.8%
66094
 
9.8%
55258
 
8.4%
74305
 
6.9%
43924
 
6.3%
23890
 
6.2%
93876
 
6.2%
33760
 
6.0%
83746
 
6.0%
03623
 
5.8%
Other values (2)262
 
0.4%

goalkeeping_positioning
Categorical

HIGH CARDINALITY

Distinct276
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
8
3343 
10
3337 
9
3313 
7
3291 
11
3261 
Other values (271)
21003 

Length

Max length5
Median length2
Mean length1.65915628
Min length1

Characters and Unicode

Total characters62298
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique133 ?
Unique (%)0.4%

Sample

1st row14
2nd row14
3rd row12
4th row9
5th row5

Common Values

ValueCountFrequency (%)
83343
8.9%
103337
8.9%
93313
8.8%
73291
8.8%
113261
8.7%
143209
8.5%
123160
8.4%
133053
8.1%
152587
6.9%
62562
 
6.8%
Other values (266)6432
17.1%

Length

2022-01-13T14:14:14.771888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
83343
8.9%
103337
8.9%
93313
8.8%
73291
8.8%
113261
8.7%
143209
8.5%
123160
8.4%
133053
8.1%
152587
6.9%
62562
 
6.8%
Other values (266)6432
17.1%

Most occurring characters

ValueCountFrequency (%)
123690
38.0%
66107
 
9.8%
54918
 
7.9%
74551
 
7.3%
44019
 
6.5%
83933
 
6.3%
03750
 
6.0%
93726
 
6.0%
23710
 
6.0%
33575
 
5.7%
Other values (2)319
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number61979
99.5%
Math Symbol212
 
0.3%
Dash Punctuation107
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
123690
38.2%
66107
 
9.9%
54918
 
7.9%
74551
 
7.3%
44019
 
6.5%
83933
 
6.3%
03750
 
6.1%
93726
 
6.0%
23710
 
6.0%
33575
 
5.8%
Dash Punctuation
ValueCountFrequency (%)
-107
100.0%
Math Symbol
ValueCountFrequency (%)
+212
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common62298
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
123690
38.0%
66107
 
9.8%
54918
 
7.9%
74551
 
7.3%
44019
 
6.5%
83933
 
6.3%
03750
 
6.0%
93726
 
6.0%
23710
 
6.0%
33575
 
5.7%
Other values (2)319
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII62298
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
123690
38.0%
66107
 
9.8%
54918
 
7.9%
74551
 
7.3%
44019
 
6.5%
83933
 
6.3%
03750
 
6.0%
93726
 
6.0%
23710
 
6.0%
33575
 
5.7%
Other values (2)319
 
0.5%

goalkeeping_reflexes
Categorical

HIGH CARDINALITY

Distinct269
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
11
3306 
9
3297 
8
3280 
12
3254 
10
3238 
Other values (264)
21173 

Length

Max length5
Median length2
Mean length1.661553212
Min length1

Characters and Unicode

Total characters62388
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique124 ?
Unique (%)0.3%

Sample

1st row12
2nd row9
3rd row8
4th row12
5th row6

Common Values

ValueCountFrequency (%)
113306
8.8%
93297
8.8%
83280
8.7%
123254
8.7%
103238
8.6%
73222
8.6%
143210
8.5%
133166
8.4%
62625
7.0%
152473
 
6.6%
Other values (259)6477
17.2%

Length

2022-01-13T14:14:14.887236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
113306
8.8%
93297
8.8%
83280
8.7%
123254
8.7%
103238
8.6%
73222
8.6%
143210
8.5%
133166
8.4%
62625
7.0%
152473
 
6.6%
Other values (259)6477
17.2%

Most occurring characters

ValueCountFrequency (%)
123797
38.1%
66102
 
9.8%
74839
 
7.8%
54580
 
7.3%
83979
 
6.4%
43871
 
6.2%
23795
 
6.1%
93748
 
6.0%
33677
 
5.9%
03661
 
5.9%
Other values (2)339
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number62049
99.5%
Math Symbol213
 
0.3%
Dash Punctuation126
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
123797
38.4%
66102
 
9.8%
74839
 
7.8%
54580
 
7.4%
83979
 
6.4%
43871
 
6.2%
23795
 
6.1%
93748
 
6.0%
33677
 
5.9%
03661
 
5.9%
Dash Punctuation
ValueCountFrequency (%)
-126
100.0%
Math Symbol
ValueCountFrequency (%)
+213
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common62388
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
123797
38.1%
66102
 
9.8%
74839
 
7.8%
54580
 
7.3%
83979
 
6.4%
43871
 
6.2%
23795
 
6.1%
93748
 
6.0%
33677
 
5.9%
03661
 
5.9%
Other values (2)339
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII62388
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
123797
38.1%
66102
 
9.8%
74839
 
7.8%
54580
 
7.3%
83979
 
6.4%
43871
 
6.2%
23795
 
6.1%
93748
 
6.0%
33677
 
5.9%
03661
 
5.9%
Other values (2)339
 
0.5%

ls
Categorical

HIGH CARDINALITY
MISSING

Distinct187
Distinct (%)0.6%
Missing4192
Missing (%)11.2%
Memory size2.1 MiB
60+2
 
984
57+2
 
980
58+2
 
955
61+2
 
945
59+2
 
939
Other values (182)
28553 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters133424
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)< 0.1%

Sample

1st row73+2
2nd row63+2
3rd row71+2
4th row67+2
5th row47+1

Common Values

ValueCountFrequency (%)
60+2984
 
2.6%
57+2980
 
2.6%
58+2955
 
2.5%
61+2945
 
2.5%
59+2939
 
2.5%
63+2934
 
2.5%
64+2902
 
2.4%
62+2887
 
2.4%
55+2790
 
2.1%
56+2783
 
2.1%
Other values (177)24257
64.6%
(Missing)4192
 
11.2%

Length

2022-01-13T14:14:15.091429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
60+2984
 
2.9%
57+2980
 
2.9%
58+2955
 
2.9%
61+2945
 
2.8%
59+2939
 
2.8%
63+2934
 
2.8%
64+2902
 
2.7%
62+2887
 
2.7%
55+2790
 
2.4%
56+2783
 
2.3%
Other values (177)24257
72.7%

Most occurring characters

ValueCountFrequency (%)
+33356
25.0%
225519
19.1%
615441
11.6%
514690
11.0%
113544
10.2%
48817
 
6.6%
76725
 
5.0%
34641
 
3.5%
03849
 
2.9%
83549
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number100068
75.0%
Math Symbol33356
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
225519
25.5%
615441
15.4%
514690
14.7%
113544
13.5%
48817
 
8.8%
76725
 
6.7%
34641
 
4.6%
03849
 
3.8%
83549
 
3.5%
93293
 
3.3%
Math Symbol
ValueCountFrequency (%)
+33356
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common133424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
+33356
25.0%
225519
19.1%
615441
11.6%
514690
11.0%
113544
10.2%
48817
 
6.6%
76725
 
5.0%
34641
 
3.5%
03849
 
2.9%
83549
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII133424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
+33356
25.0%
225519
19.1%
615441
11.6%
514690
11.0%
113544
10.2%
48817
 
6.6%
76725
 
5.0%
34641
 
3.5%
03849
 
2.9%
83549
 
2.7%

st
Categorical

HIGH CARDINALITY
MISSING

Distinct187
Distinct (%)0.6%
Missing4192
Missing (%)11.2%
Memory size2.1 MiB
60+2
 
984
57+2
 
980
58+2
 
955
61+2
 
945
59+2
 
939
Other values (182)
28553 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters133424
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)< 0.1%

Sample

1st row73+2
2nd row63+2
3rd row71+2
4th row67+2
5th row47+1

Common Values

ValueCountFrequency (%)
60+2984
 
2.6%
57+2980
 
2.6%
58+2955
 
2.5%
61+2945
 
2.5%
59+2939
 
2.5%
63+2934
 
2.5%
64+2902
 
2.4%
62+2887
 
2.4%
55+2790
 
2.1%
56+2783
 
2.1%
Other values (177)24257
64.6%
(Missing)4192
 
11.2%

Length

2022-01-13T14:14:15.213196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
60+2984
 
2.9%
57+2980
 
2.9%
58+2955
 
2.9%
61+2945
 
2.8%
59+2939
 
2.8%
63+2934
 
2.8%
64+2902
 
2.7%
62+2887
 
2.7%
55+2790
 
2.4%
56+2783
 
2.3%
Other values (177)24257
72.7%

Most occurring characters

ValueCountFrequency (%)
+33356
25.0%
225519
19.1%
615441
11.6%
514690
11.0%
113544
10.2%
48817
 
6.6%
76725
 
5.0%
34641
 
3.5%
03849
 
2.9%
83549
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number100068
75.0%
Math Symbol33356
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
225519
25.5%
615441
15.4%
514690
14.7%
113544
13.5%
48817
 
8.8%
76725
 
6.7%
34641
 
4.6%
03849
 
3.8%
83549
 
3.5%
93293
 
3.3%
Math Symbol
ValueCountFrequency (%)
+33356
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common133424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
+33356
25.0%
225519
19.1%
615441
11.6%
514690
11.0%
113544
10.2%
48817
 
6.6%
76725
 
5.0%
34641
 
3.5%
03849
 
2.9%
83549
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII133424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
+33356
25.0%
225519
19.1%
615441
11.6%
514690
11.0%
113544
10.2%
48817
 
6.6%
76725
 
5.0%
34641
 
3.5%
03849
 
2.9%
83549
 
2.7%

rs
Categorical

HIGH CARDINALITY
MISSING

Distinct187
Distinct (%)0.6%
Missing4192
Missing (%)11.2%
Memory size2.1 MiB
60+2
 
984
57+2
 
980
58+2
 
955
61+2
 
945
59+2
 
939
Other values (182)
28553 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters133424
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)< 0.1%

Sample

1st row73+2
2nd row63+2
3rd row71+2
4th row67+2
5th row47+1

Common Values

ValueCountFrequency (%)
60+2984
 
2.6%
57+2980
 
2.6%
58+2955
 
2.5%
61+2945
 
2.5%
59+2939
 
2.5%
63+2934
 
2.5%
64+2902
 
2.4%
62+2887
 
2.4%
55+2790
 
2.1%
56+2783
 
2.1%
Other values (177)24257
64.6%
(Missing)4192
 
11.2%

Length

2022-01-13T14:14:16.580133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
60+2984
 
2.9%
57+2980
 
2.9%
58+2955
 
2.9%
61+2945
 
2.8%
59+2939
 
2.8%
63+2934
 
2.8%
64+2902
 
2.7%
62+2887
 
2.7%
55+2790
 
2.4%
56+2783
 
2.3%
Other values (177)24257
72.7%

Most occurring characters

ValueCountFrequency (%)
+33356
25.0%
225519
19.1%
615441
11.6%
514690
11.0%
113544
10.2%
48817
 
6.6%
76725
 
5.0%
34641
 
3.5%
03849
 
2.9%
83549
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number100068
75.0%
Math Symbol33356
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
225519
25.5%
615441
15.4%
514690
14.7%
113544
13.5%
48817
 
8.8%
76725
 
6.7%
34641
 
4.6%
03849
 
3.8%
83549
 
3.5%
93293
 
3.3%
Math Symbol
ValueCountFrequency (%)
+33356
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common133424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
+33356
25.0%
225519
19.1%
615441
11.6%
514690
11.0%
113544
10.2%
48817
 
6.6%
76725
 
5.0%
34641
 
3.5%
03849
 
2.9%
83549
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII133424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
+33356
25.0%
225519
19.1%
615441
11.6%
514690
11.0%
113544
10.2%
48817
 
6.6%
76725
 
5.0%
34641
 
3.5%
03849
 
2.9%
83549
 
2.7%

lw
Categorical

HIGH CARDINALITY
MISSING

Distinct210
Distinct (%)0.6%
Missing4192
Missing (%)11.2%
Memory size2.1 MiB
64+2
 
1002
63+2
 
983
61+2
 
982
59+2
 
960
60+2
 
958
Other values (205)
28471 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters133424
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)0.1%

Sample

1st row74+2
2nd row66+2
3rd row74+2
4th row61+2
5th row48+1

Common Values

ValueCountFrequency (%)
64+21002
 
2.7%
63+2983
 
2.6%
61+2982
 
2.6%
59+2960
 
2.6%
60+2958
 
2.6%
62+2941
 
2.5%
65+2924
 
2.5%
58+2872
 
2.3%
66+2857
 
2.3%
67+2796
 
2.1%
Other values (200)24081
64.1%
(Missing)4192
 
11.2%

Length

2022-01-13T14:14:16.771948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
64+21002
 
3.0%
63+2983
 
2.9%
61+2982
 
2.9%
59+2960
 
2.9%
60+2958
 
2.9%
62+2941
 
2.8%
65+2924
 
2.8%
58+2872
 
2.6%
66+2857
 
2.6%
67+2796
 
2.4%
Other values (200)24081
72.2%

Most occurring characters

ValueCountFrequency (%)
+33356
25.0%
225560
19.2%
616560
12.4%
113497
10.1%
513354
10.0%
77625
 
5.7%
47468
 
5.6%
35232
 
3.9%
03766
 
2.8%
83651
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number100068
75.0%
Math Symbol33356
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
225560
25.5%
616560
16.5%
113497
13.5%
513354
13.3%
77625
 
7.6%
47468
 
7.5%
35232
 
5.2%
03766
 
3.8%
83651
 
3.6%
93355
 
3.4%
Math Symbol
ValueCountFrequency (%)
+33356
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common133424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
+33356
25.0%
225560
19.2%
616560
12.4%
113497
10.1%
513354
10.0%
77625
 
5.7%
47468
 
5.6%
35232
 
3.9%
03766
 
2.8%
83651
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII133424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
+33356
25.0%
225560
19.2%
616560
12.4%
113497
10.1%
513354
10.0%
77625
 
5.7%
47468
 
5.6%
35232
 
3.9%
03766
 
2.8%
83651
 
2.7%

lf
Categorical

HIGH CARDINALITY
MISSING

Distinct201
Distinct (%)0.6%
Missing4192
Missing (%)11.2%
Memory size2.1 MiB
63+2
 
993
61+2
 
980
60+2
 
977
62+2
 
973
59+2
 
938
Other values (196)
28495 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters133424
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)0.1%

Sample

1st row75+2
2nd row65+2
3rd row73+2
4th row64+2
5th row48+1

Common Values

ValueCountFrequency (%)
63+2993
 
2.6%
61+2980
 
2.6%
60+2977
 
2.6%
62+2973
 
2.6%
59+2938
 
2.5%
64+2907
 
2.4%
65+2897
 
2.4%
58+2856
 
2.3%
66+2821
 
2.2%
57+2800
 
2.1%
Other values (191)24214
64.5%
(Missing)4192
 
11.2%

Length

2022-01-13T14:14:16.881401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
63+2993
 
3.0%
61+2980
 
2.9%
60+2977
 
2.9%
62+2973
 
2.9%
59+2938
 
2.8%
64+2907
 
2.7%
65+2897
 
2.7%
58+2856
 
2.6%
66+2821
 
2.5%
57+2800
 
2.4%
Other values (191)24214
72.6%

Most occurring characters

ValueCountFrequency (%)
+33356
25.0%
225558
19.2%
616288
12.2%
113602
10.2%
513444
10.1%
47725
 
5.8%
77430
 
5.6%
35173
 
3.9%
03881
 
2.9%
83660
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number100068
75.0%
Math Symbol33356
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
225558
25.5%
616288
16.3%
113602
13.6%
513444
13.4%
47725
 
7.7%
77430
 
7.4%
35173
 
5.2%
03881
 
3.9%
83660
 
3.7%
93307
 
3.3%
Math Symbol
ValueCountFrequency (%)
+33356
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common133424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
+33356
25.0%
225558
19.2%
616288
12.2%
113602
10.2%
513444
10.1%
47725
 
5.8%
77430
 
5.6%
35173
 
3.9%
03881
 
2.9%
83660
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII133424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
+33356
25.0%
225558
19.2%
616288
12.2%
113602
10.2%
513444
10.1%
47725
 
5.8%
77430
 
5.6%
35173
 
3.9%
03881
 
2.9%
83660
 
2.7%

cf
Categorical

HIGH CARDINALITY
MISSING

Distinct201
Distinct (%)0.6%
Missing4192
Missing (%)11.2%
Memory size2.1 MiB
63+2
 
993
61+2
 
980
60+2
 
977
62+2
 
973
59+2
 
938
Other values (196)
28495 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters133424
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)0.1%

Sample

1st row75+2
2nd row65+2
3rd row73+2
4th row64+2
5th row48+1

Common Values

ValueCountFrequency (%)
63+2993
 
2.6%
61+2980
 
2.6%
60+2977
 
2.6%
62+2973
 
2.6%
59+2938
 
2.5%
64+2907
 
2.4%
65+2897
 
2.4%
58+2856
 
2.3%
66+2821
 
2.2%
57+2800
 
2.1%
Other values (191)24214
64.5%
(Missing)4192
 
11.2%

Length

2022-01-13T14:14:17.022449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
63+2993
 
3.0%
61+2980
 
2.9%
60+2977
 
2.9%
62+2973
 
2.9%
59+2938
 
2.8%
64+2907
 
2.7%
65+2897
 
2.7%
58+2856
 
2.6%
66+2821
 
2.5%
57+2800
 
2.4%
Other values (191)24214
72.6%

Most occurring characters

ValueCountFrequency (%)
+33356
25.0%
225558
19.2%
616288
12.2%
113602
10.2%
513444
10.1%
47725
 
5.8%
77430
 
5.6%
35173
 
3.9%
03881
 
2.9%
83660
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number100068
75.0%
Math Symbol33356
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
225558
25.5%
616288
16.3%
113602
13.6%
513444
13.4%
47725
 
7.7%
77430
 
7.4%
35173
 
5.2%
03881
 
3.9%
83660
 
3.7%
93307
 
3.3%
Math Symbol
ValueCountFrequency (%)
+33356
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common133424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
+33356
25.0%
225558
19.2%
616288
12.2%
113602
10.2%
513444
10.1%
47725
 
5.8%
77430
 
5.6%
35173
 
3.9%
03881
 
2.9%
83660
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII133424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
+33356
25.0%
225558
19.2%
616288
12.2%
113602
10.2%
513444
10.1%
47725
 
5.8%
77430
 
5.6%
35173
 
3.9%
03881
 
2.9%
83660
 
2.7%

rf
Categorical

HIGH CARDINALITY
MISSING

Distinct201
Distinct (%)0.6%
Missing4192
Missing (%)11.2%
Memory size2.1 MiB
63+2
 
993
61+2
 
980
60+2
 
977
62+2
 
973
59+2
 
938
Other values (196)
28495 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters133424
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)0.1%

Sample

1st row75+2
2nd row65+2
3rd row73+2
4th row64+2
5th row48+1

Common Values

ValueCountFrequency (%)
63+2993
 
2.6%
61+2980
 
2.6%
60+2977
 
2.6%
62+2973
 
2.6%
59+2938
 
2.5%
64+2907
 
2.4%
65+2897
 
2.4%
58+2856
 
2.3%
66+2821
 
2.2%
57+2800
 
2.1%
Other values (191)24214
64.5%
(Missing)4192
 
11.2%

Length

2022-01-13T14:14:17.143187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
63+2993
 
3.0%
61+2980
 
2.9%
60+2977
 
2.9%
62+2973
 
2.9%
59+2938
 
2.8%
64+2907
 
2.7%
65+2897
 
2.7%
58+2856
 
2.6%
66+2821
 
2.5%
57+2800
 
2.4%
Other values (191)24214
72.6%

Most occurring characters

ValueCountFrequency (%)
+33356
25.0%
225558
19.2%
616288
12.2%
113602
10.2%
513444
10.1%
47725
 
5.8%
77430
 
5.6%
35173
 
3.9%
03881
 
2.9%
83660
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number100068
75.0%
Math Symbol33356
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
225558
25.5%
616288
16.3%
113602
13.6%
513444
13.4%
47725
 
7.7%
77430
 
7.4%
35173
 
5.2%
03881
 
3.9%
83660
 
3.7%
93307
 
3.3%
Math Symbol
ValueCountFrequency (%)
+33356
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common133424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
+33356
25.0%
225558
19.2%
616288
12.2%
113602
10.2%
513444
10.1%
47725
 
5.8%
77430
 
5.6%
35173
 
3.9%
03881
 
2.9%
83660
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII133424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
+33356
25.0%
225558
19.2%
616288
12.2%
113602
10.2%
513444
10.1%
47725
 
5.8%
77430
 
5.6%
35173
 
3.9%
03881
 
2.9%
83660
 
2.7%

rw
Categorical

HIGH CARDINALITY
MISSING

Distinct210
Distinct (%)0.6%
Missing4192
Missing (%)11.2%
Memory size2.1 MiB
64+2
 
1002
63+2
 
983
61+2
 
982
59+2
 
960
60+2
 
958
Other values (205)
28471 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters133424
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)0.1%

Sample

1st row74+2
2nd row66+2
3rd row74+2
4th row61+2
5th row48+1

Common Values

ValueCountFrequency (%)
64+21002
 
2.7%
63+2983
 
2.6%
61+2982
 
2.6%
59+2960
 
2.6%
60+2958
 
2.6%
62+2941
 
2.5%
65+2924
 
2.5%
58+2872
 
2.3%
66+2857
 
2.3%
67+2796
 
2.1%
Other values (200)24081
64.1%
(Missing)4192
 
11.2%

Length

2022-01-13T14:14:17.554982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
64+21002
 
3.0%
63+2983
 
2.9%
61+2982
 
2.9%
59+2960
 
2.9%
60+2958
 
2.9%
62+2941
 
2.8%
65+2924
 
2.8%
58+2872
 
2.6%
66+2857
 
2.6%
67+2796
 
2.4%
Other values (200)24081
72.2%

Most occurring characters

ValueCountFrequency (%)
+33356
25.0%
225560
19.2%
616560
12.4%
113497
10.1%
513354
10.0%
77625
 
5.7%
47468
 
5.6%
35232
 
3.9%
03766
 
2.8%
83651
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number100068
75.0%
Math Symbol33356
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
225560
25.5%
616560
16.5%
113497
13.5%
513354
13.3%
77625
 
7.6%
47468
 
7.5%
35232
 
5.2%
03766
 
3.8%
83651
 
3.6%
93355
 
3.4%
Math Symbol
ValueCountFrequency (%)
+33356
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common133424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
+33356
25.0%
225560
19.2%
616560
12.4%
113497
10.1%
513354
10.0%
77625
 
5.7%
47468
 
5.6%
35232
 
3.9%
03766
 
2.8%
83651
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII133424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
+33356
25.0%
225560
19.2%
616560
12.4%
113497
10.1%
513354
10.0%
77625
 
5.7%
47468
 
5.6%
35232
 
3.9%
03766
 
2.8%
83651
 
2.7%

lam
Categorical

HIGH CARDINALITY
MISSING

Distinct206
Distinct (%)0.6%
Missing4192
Missing (%)11.2%
Memory size2.1 MiB
63+2
 
1024
61+2
 
999
62+2
 
991
60+2
 
970
59+2
 
953
Other values (201)
28419 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters133424
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)0.1%

Sample

1st row76+2
2nd row67+2
3rd row73+2
4th row62+2
5th row49+1

Common Values

ValueCountFrequency (%)
63+21024
 
2.7%
61+2999
 
2.7%
62+2991
 
2.6%
60+2970
 
2.6%
59+2953
 
2.5%
64+2934
 
2.5%
58+2909
 
2.4%
66+2853
 
2.3%
57+2847
 
2.3%
65+2847
 
2.3%
Other values (196)24029
64.0%
(Missing)4192
 
11.2%

Length

2022-01-13T14:14:18.122500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
63+21024
 
3.1%
61+2999
 
3.0%
62+2991
 
3.0%
60+2970
 
2.9%
59+2953
 
2.9%
64+2934
 
2.8%
58+2909
 
2.7%
66+2853
 
2.6%
65+2847
 
2.5%
57+2847
 
2.5%
Other values (196)24029
72.0%

Most occurring characters

ValueCountFrequency (%)
+33356
25.0%
225587
19.2%
616328
12.2%
513524
10.1%
113504
10.1%
77620
 
5.7%
47464
 
5.6%
35232
 
3.9%
03885
 
2.9%
83629
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number100068
75.0%
Math Symbol33356
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
225587
25.6%
616328
16.3%
513524
13.5%
113504
13.5%
77620
 
7.6%
47464
 
7.5%
35232
 
5.2%
03885
 
3.9%
83629
 
3.6%
93295
 
3.3%
Math Symbol
ValueCountFrequency (%)
+33356
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common133424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
+33356
25.0%
225587
19.2%
616328
12.2%
513524
10.1%
113504
10.1%
77620
 
5.7%
47464
 
5.6%
35232
 
3.9%
03885
 
2.9%
83629
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII133424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
+33356
25.0%
225587
19.2%
616328
12.2%
513524
10.1%
113504
10.1%
77620
 
5.7%
47464
 
5.6%
35232
 
3.9%
03885
 
2.9%
83629
 
2.7%

cam
Categorical

HIGH CARDINALITY
MISSING

Distinct206
Distinct (%)0.6%
Missing4192
Missing (%)11.2%
Memory size2.1 MiB
63+2
 
1024
61+2
 
999
62+2
 
991
60+2
 
970
59+2
 
953
Other values (201)
28419 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters133424
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)0.1%

Sample

1st row76+2
2nd row67+2
3rd row73+2
4th row62+2
5th row49+1

Common Values

ValueCountFrequency (%)
63+21024
 
2.7%
61+2999
 
2.7%
62+2991
 
2.6%
60+2970
 
2.6%
59+2953
 
2.5%
64+2934
 
2.5%
58+2909
 
2.4%
66+2853
 
2.3%
57+2847
 
2.3%
65+2847
 
2.3%
Other values (196)24029
64.0%
(Missing)4192
 
11.2%

Length

2022-01-13T14:14:18.244983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
63+21024
 
3.1%
61+2999
 
3.0%
62+2991
 
3.0%
60+2970
 
2.9%
59+2953
 
2.9%
64+2934
 
2.8%
58+2909
 
2.7%
66+2853
 
2.6%
65+2847
 
2.5%
57+2847
 
2.5%
Other values (196)24029
72.0%

Most occurring characters

ValueCountFrequency (%)
+33356
25.0%
225587
19.2%
616328
12.2%
513524
10.1%
113504
10.1%
77620
 
5.7%
47464
 
5.6%
35232
 
3.9%
03885
 
2.9%
83629
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number100068
75.0%
Math Symbol33356
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
225587
25.6%
616328
16.3%
513524
13.5%
113504
13.5%
77620
 
7.6%
47464
 
7.5%
35232
 
5.2%
03885
 
3.9%
83629
 
3.6%
93295
 
3.3%
Math Symbol
ValueCountFrequency (%)
+33356
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common133424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
+33356
25.0%
225587
19.2%
616328
12.2%
513524
10.1%
113504
10.1%
77620
 
5.7%
47464
 
5.6%
35232
 
3.9%
03885
 
2.9%
83629
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII133424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
+33356
25.0%
225587
19.2%
616328
12.2%
513524
10.1%
113504
10.1%
77620
 
5.7%
47464
 
5.6%
35232
 
3.9%
03885
 
2.9%
83629
 
2.7%

ram
Categorical

HIGH CARDINALITY
MISSING

Distinct206
Distinct (%)0.6%
Missing4192
Missing (%)11.2%
Memory size2.1 MiB
63+2
 
1024
61+2
 
999
62+2
 
991
60+2
 
970
59+2
 
953
Other values (201)
28419 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters133424
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)0.1%

Sample

1st row76+2
2nd row67+2
3rd row73+2
4th row62+2
5th row49+1

Common Values

ValueCountFrequency (%)
63+21024
 
2.7%
61+2999
 
2.7%
62+2991
 
2.6%
60+2970
 
2.6%
59+2953
 
2.5%
64+2934
 
2.5%
58+2909
 
2.4%
66+2853
 
2.3%
57+2847
 
2.3%
65+2847
 
2.3%
Other values (196)24029
64.0%
(Missing)4192
 
11.2%

Length

2022-01-13T14:14:18.384118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
63+21024
 
3.1%
61+2999
 
3.0%
62+2991
 
3.0%
60+2970
 
2.9%
59+2953
 
2.9%
64+2934
 
2.8%
58+2909
 
2.7%
66+2853
 
2.6%
65+2847
 
2.5%
57+2847
 
2.5%
Other values (196)24029
72.0%

Most occurring characters

ValueCountFrequency (%)
+33356
25.0%
225587
19.2%
616328
12.2%
513524
10.1%
113504
10.1%
77620
 
5.7%
47464
 
5.6%
35232
 
3.9%
03885
 
2.9%
83629
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number100068
75.0%
Math Symbol33356
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
225587
25.6%
616328
16.3%
513524
13.5%
113504
13.5%
77620
 
7.6%
47464
 
7.5%
35232
 
5.2%
03885
 
3.9%
83629
 
3.6%
93295
 
3.3%
Math Symbol
ValueCountFrequency (%)
+33356
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common133424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
+33356
25.0%
225587
19.2%
616328
12.2%
513524
10.1%
113504
10.1%
77620
 
5.7%
47464
 
5.6%
35232
 
3.9%
03885
 
2.9%
83629
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII133424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
+33356
25.0%
225587
19.2%
616328
12.2%
513524
10.1%
113504
10.1%
77620
 
5.7%
47464
 
5.6%
35232
 
3.9%
03885
 
2.9%
83629
 
2.7%

lm
Categorical

HIGH CARDINALITY
MISSING

Distinct199
Distinct (%)0.6%
Missing4192
Missing (%)11.2%
Memory size2.1 MiB
61+2
 
1114
63+2
 
1033
62+2
 
1029
65+2
 
1026
60+2
 
989
Other values (194)
28165 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters133424
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)0.1%

Sample

1st row74+2
2nd row68+2
3rd row72+2
4th row59+2
5th row50+1

Common Values

ValueCountFrequency (%)
61+21114
 
3.0%
63+21033
 
2.8%
62+21029
 
2.7%
65+21026
 
2.7%
60+2989
 
2.6%
59+2980
 
2.6%
64+2978
 
2.6%
66+2936
 
2.5%
58+2872
 
2.3%
57+2845
 
2.3%
Other values (189)23554
62.7%
(Missing)4192
 
11.2%

Length

2022-01-13T14:14:18.493757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
61+21114
 
3.3%
63+21033
 
3.1%
62+21029
 
3.1%
65+21026
 
3.1%
60+2989
 
3.0%
59+2980
 
2.9%
64+2978
 
2.9%
66+2936
 
2.8%
58+2872
 
2.6%
57+2845
 
2.5%
Other values (189)23554
70.6%

Most occurring characters

ValueCountFrequency (%)
+33356
25.0%
225582
19.2%
617239
12.9%
113598
10.2%
513399
10.0%
77809
 
5.9%
47164
 
5.4%
34706
 
3.5%
03719
 
2.8%
83548
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number100068
75.0%
Math Symbol33356
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
225582
25.6%
617239
17.2%
113598
13.6%
513399
13.4%
77809
 
7.8%
47164
 
7.2%
34706
 
4.7%
03719
 
3.7%
83548
 
3.5%
93304
 
3.3%
Math Symbol
ValueCountFrequency (%)
+33356
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common133424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
+33356
25.0%
225582
19.2%
617239
12.9%
113598
10.2%
513399
10.0%
77809
 
5.9%
47164
 
5.4%
34706
 
3.5%
03719
 
2.8%
83548
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII133424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
+33356
25.0%
225582
19.2%
617239
12.9%
113598
10.2%
513399
10.0%
77809
 
5.9%
47164
 
5.4%
34706
 
3.5%
03719
 
2.8%
83548
 
2.7%

lcm
Categorical

HIGH CARDINALITY
MISSING

Distinct184
Distinct (%)0.6%
Missing4192
Missing (%)11.2%
Memory size2.1 MiB
58+2
 
1072
60+2
 
1026
59+2
 
1001
61+2
 
1000
62+2
 
962
Other values (179)
28295 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters133424
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)< 0.1%

Sample

1st row78+2
2nd row69+2
3rd row68+2
4th row57+2
5th row52+1

Common Values

ValueCountFrequency (%)
58+21072
 
2.9%
60+21026
 
2.7%
59+21001
 
2.7%
61+21000
 
2.7%
62+2962
 
2.6%
57+2954
 
2.5%
56+2943
 
2.5%
63+2923
 
2.5%
64+2879
 
2.3%
54+2876
 
2.3%
Other values (174)23720
63.2%
(Missing)4192
 
11.2%

Length

2022-01-13T14:14:18.626901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
58+21072
 
3.2%
60+21026
 
3.1%
59+21001
 
3.0%
61+21000
 
3.0%
62+2962
 
2.9%
57+2954
 
2.9%
56+2943
 
2.8%
63+2923
 
2.8%
64+2879
 
2.6%
54+2876
 
2.6%
Other values (174)23720
71.1%

Most occurring characters

ValueCountFrequency (%)
+33356
25.0%
225468
19.1%
515521
11.6%
615480
11.6%
113562
10.2%
48144
 
6.1%
76603
 
4.9%
34552
 
3.4%
03904
 
2.9%
83586
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number100068
75.0%
Math Symbol33356
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
225468
25.5%
515521
15.5%
615480
15.5%
113562
13.6%
48144
 
8.1%
76603
 
6.6%
34552
 
4.5%
03904
 
3.9%
83586
 
3.6%
93248
 
3.2%
Math Symbol
ValueCountFrequency (%)
+33356
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common133424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
+33356
25.0%
225468
19.1%
515521
11.6%
615480
11.6%
113562
10.2%
48144
 
6.1%
76603
 
4.9%
34552
 
3.4%
03904
 
2.9%
83586
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII133424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
+33356
25.0%
225468
19.1%
515521
11.6%
615480
11.6%
113562
10.2%
48144
 
6.1%
76603
 
4.9%
34552
 
3.4%
03904
 
2.9%
83586
 
2.7%

cm
Categorical

HIGH CARDINALITY
MISSING

Distinct184
Distinct (%)0.6%
Missing4192
Missing (%)11.2%
Memory size2.1 MiB
58+2
 
1072
60+2
 
1026
59+2
 
1001
61+2
 
1000
62+2
 
962
Other values (179)
28295 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters133424
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)< 0.1%

Sample

1st row78+2
2nd row69+2
3rd row68+2
4th row57+2
5th row52+1

Common Values

ValueCountFrequency (%)
58+21072
 
2.9%
60+21026
 
2.7%
59+21001
 
2.7%
61+21000
 
2.7%
62+2962
 
2.6%
57+2954
 
2.5%
56+2943
 
2.5%
63+2923
 
2.5%
64+2879
 
2.3%
54+2876
 
2.3%
Other values (174)23720
63.2%
(Missing)4192
 
11.2%

Length

2022-01-13T14:14:18.749188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
58+21072
 
3.2%
60+21026
 
3.1%
59+21001
 
3.0%
61+21000
 
3.0%
62+2962
 
2.9%
57+2954
 
2.9%
56+2943
 
2.8%
63+2923
 
2.8%
64+2879
 
2.6%
54+2876
 
2.6%
Other values (174)23720
71.1%

Most occurring characters

ValueCountFrequency (%)
+33356
25.0%
225468
19.1%
515521
11.6%
615480
11.6%
113562
10.2%
48144
 
6.1%
76603
 
4.9%
34552
 
3.4%
03904
 
2.9%
83586
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number100068
75.0%
Math Symbol33356
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
225468
25.5%
515521
15.5%
615480
15.5%
113562
13.6%
48144
 
8.1%
76603
 
6.6%
34552
 
4.5%
03904
 
3.9%
83586
 
3.6%
93248
 
3.2%
Math Symbol
ValueCountFrequency (%)
+33356
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common133424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
+33356
25.0%
225468
19.1%
515521
11.6%
615480
11.6%
113562
10.2%
48144
 
6.1%
76603
 
4.9%
34552
 
3.4%
03904
 
2.9%
83586
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII133424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
+33356
25.0%
225468
19.1%
515521
11.6%
615480
11.6%
113562
10.2%
48144
 
6.1%
76603
 
4.9%
34552
 
3.4%
03904
 
2.9%
83586
 
2.7%

rcm
Categorical

HIGH CARDINALITY
MISSING

Distinct184
Distinct (%)0.6%
Missing4192
Missing (%)11.2%
Memory size2.1 MiB
58+2
 
1072
60+2
 
1026
59+2
 
1001
61+2
 
1000
62+2
 
962
Other values (179)
28295 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters133424
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)< 0.1%

Sample

1st row78+2
2nd row69+2
3rd row68+2
4th row57+2
5th row52+1

Common Values

ValueCountFrequency (%)
58+21072
 
2.9%
60+21026
 
2.7%
59+21001
 
2.7%
61+21000
 
2.7%
62+2962
 
2.6%
57+2954
 
2.5%
56+2943
 
2.5%
63+2923
 
2.5%
64+2879
 
2.3%
54+2876
 
2.3%
Other values (174)23720
63.2%
(Missing)4192
 
11.2%

Length

2022-01-13T14:14:18.859834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
58+21072
 
3.2%
60+21026
 
3.1%
59+21001
 
3.0%
61+21000
 
3.0%
62+2962
 
2.9%
57+2954
 
2.9%
56+2943
 
2.8%
63+2923
 
2.8%
64+2879
 
2.6%
54+2876
 
2.6%
Other values (174)23720
71.1%

Most occurring characters

ValueCountFrequency (%)
+33356
25.0%
225468
19.1%
515521
11.6%
615480
11.6%
113562
10.2%
48144
 
6.1%
76603
 
4.9%
34552
 
3.4%
03904
 
2.9%
83586
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number100068
75.0%
Math Symbol33356
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
225468
25.5%
515521
15.5%
615480
15.5%
113562
13.6%
48144
 
8.1%
76603
 
6.6%
34552
 
4.5%
03904
 
3.9%
83586
 
3.6%
93248
 
3.2%
Math Symbol
ValueCountFrequency (%)
+33356
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common133424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
+33356
25.0%
225468
19.1%
515521
11.6%
615480
11.6%
113562
10.2%
48144
 
6.1%
76603
 
4.9%
34552
 
3.4%
03904
 
2.9%
83586
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII133424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
+33356
25.0%
225468
19.1%
515521
11.6%
615480
11.6%
113562
10.2%
48144
 
6.1%
76603
 
4.9%
34552
 
3.4%
03904
 
2.9%
83586
 
2.7%

rm
Categorical

HIGH CARDINALITY
MISSING

Distinct199
Distinct (%)0.6%
Missing4192
Missing (%)11.2%
Memory size2.1 MiB
61+2
 
1114
63+2
 
1033
62+2
 
1029
65+2
 
1026
60+2
 
989
Other values (194)
28165 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters133424
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)0.1%

Sample

1st row74+2
2nd row68+2
3rd row72+2
4th row59+2
5th row50+1

Common Values

ValueCountFrequency (%)
61+21114
 
3.0%
63+21033
 
2.8%
62+21029
 
2.7%
65+21026
 
2.7%
60+2989
 
2.6%
59+2980
 
2.6%
64+2978
 
2.6%
66+2936
 
2.5%
58+2872
 
2.3%
57+2845
 
2.3%
Other values (189)23554
62.7%
(Missing)4192
 
11.2%

Length

2022-01-13T14:14:18.969741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
61+21114
 
3.3%
63+21033
 
3.1%
62+21029
 
3.1%
65+21026
 
3.1%
60+2989
 
3.0%
59+2980
 
2.9%
64+2978
 
2.9%
66+2936
 
2.8%
58+2872
 
2.6%
57+2845
 
2.5%
Other values (189)23554
70.6%

Most occurring characters

ValueCountFrequency (%)
+33356
25.0%
225582
19.2%
617239
12.9%
113598
10.2%
513399
10.0%
77809
 
5.9%
47164
 
5.4%
34706
 
3.5%
03719
 
2.8%
83548
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number100068
75.0%
Math Symbol33356
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
225582
25.6%
617239
17.2%
113598
13.6%
513399
13.4%
77809
 
7.8%
47164
 
7.2%
34706
 
4.7%
03719
 
3.7%
83548
 
3.5%
93304
 
3.3%
Math Symbol
ValueCountFrequency (%)
+33356
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common133424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
+33356
25.0%
225582
19.2%
617239
12.9%
113598
10.2%
513399
10.0%
77809
 
5.9%
47164
 
5.4%
34706
 
3.5%
03719
 
2.8%
83548
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII133424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
+33356
25.0%
225582
19.2%
617239
12.9%
113598
10.2%
513399
10.0%
77809
 
5.9%
47164
 
5.4%
34706
 
3.5%
03719
 
2.8%
83548
 
2.7%

lwb
Categorical

HIGH CARDINALITY
MISSING

Distinct188
Distinct (%)0.6%
Missing4192
Missing (%)11.2%
Memory size2.1 MiB
59+2
 
953
61+2
 
931
62+2
 
913
60+2
 
898
55+2
 
860
Other values (183)
28801 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters133424
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)0.1%

Sample

1st row71+2
2nd row71+2
3rd row60+2
4th row43+2
5th row56+1

Common Values

ValueCountFrequency (%)
59+2953
 
2.5%
61+2931
 
2.5%
62+2913
 
2.4%
60+2898
 
2.4%
55+2860
 
2.3%
57+2859
 
2.3%
54+2857
 
2.3%
63+2853
 
2.3%
56+2852
 
2.3%
53+2837
 
2.2%
Other values (178)24543
65.4%
(Missing)4192
 
11.2%

Length

2022-01-13T14:14:19.080629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
59+2953
 
2.9%
61+2931
 
2.8%
62+2913
 
2.7%
60+2898
 
2.7%
55+2860
 
2.6%
57+2859
 
2.6%
54+2857
 
2.6%
63+2853
 
2.6%
56+2852
 
2.6%
53+2837
 
2.5%
Other values (178)24543
73.6%

Most occurring characters

ValueCountFrequency (%)
+33356
25.0%
225535
19.1%
515578
11.7%
614744
11.1%
113547
10.2%
49037
 
6.8%
76329
 
4.7%
34725
 
3.5%
03864
 
2.9%
93373
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number100068
75.0%
Math Symbol33356
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
225535
25.5%
515578
15.6%
614744
14.7%
113547
13.5%
49037
 
9.0%
76329
 
6.3%
34725
 
4.7%
03864
 
3.9%
93373
 
3.4%
83336
 
3.3%
Math Symbol
ValueCountFrequency (%)
+33356
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common133424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
+33356
25.0%
225535
19.1%
515578
11.7%
614744
11.1%
113547
10.2%
49037
 
6.8%
76329
 
4.7%
34725
 
3.5%
03864
 
2.9%
93373
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII133424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
+33356
25.0%
225535
19.1%
515578
11.7%
614744
11.1%
113547
10.2%
49037
 
6.8%
76329
 
4.7%
34725
 
3.5%
03864
 
2.9%
93373
 
2.5%

ldm
Categorical

HIGH CARDINALITY
MISSING

Distinct198
Distinct (%)0.6%
Missing4192
Missing (%)11.2%
Memory size2.1 MiB
62+2
 
870
59+2
 
824
63+2
 
822
61+2
 
821
60+2
 
809
Other values (193)
29210 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters133424
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)< 0.1%

Sample

1st row74+2
2nd row71+2
3rd row60+2
4th row46+2
5th row57+1

Common Values

ValueCountFrequency (%)
62+2870
 
2.3%
59+2824
 
2.2%
63+2822
 
2.2%
61+2821
 
2.2%
60+2809
 
2.2%
58+2804
 
2.1%
64+2776
 
2.1%
57+2745
 
2.0%
65+2714
 
1.9%
66+2706
 
1.9%
Other values (188)25465
67.8%
(Missing)4192
 
11.2%

Length

2022-01-13T14:14:19.178167image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
62+2870
 
2.6%
59+2824
 
2.5%
63+2822
 
2.5%
61+2821
 
2.5%
60+2809
 
2.4%
58+2804
 
2.4%
64+2776
 
2.3%
57+2745
 
2.2%
65+2714
 
2.1%
66+2706
 
2.1%
Other values (188)25465
76.3%

Most occurring characters

ValueCountFrequency (%)
+33356
25.0%
225526
19.1%
614344
10.8%
513709
10.3%
113472
10.1%
410052
 
7.5%
76740
 
5.1%
35595
 
4.2%
03748
 
2.8%
83552
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number100068
75.0%
Math Symbol33356
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
225526
25.5%
614344
14.3%
513709
13.7%
113472
13.5%
410052
 
10.0%
76740
 
6.7%
35595
 
5.6%
03748
 
3.7%
83552
 
3.5%
93330
 
3.3%
Math Symbol
ValueCountFrequency (%)
+33356
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common133424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
+33356
25.0%
225526
19.1%
614344
10.8%
513709
10.3%
113472
10.1%
410052
 
7.5%
76740
 
5.1%
35595
 
4.2%
03748
 
2.8%
83552
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII133424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
+33356
25.0%
225526
19.1%
614344
10.8%
513709
10.3%
113472
10.1%
410052
 
7.5%
76740
 
5.1%
35595
 
4.2%
03748
 
2.8%
83552
 
2.7%

cdm
Categorical

HIGH CARDINALITY
MISSING

Distinct198
Distinct (%)0.6%
Missing4192
Missing (%)11.2%
Memory size2.1 MiB
62+2
 
870
59+2
 
824
63+2
 
822
61+2
 
821
60+2
 
809
Other values (193)
29210 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters133424
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)< 0.1%

Sample

1st row74+2
2nd row71+2
3rd row60+2
4th row46+2
5th row57+1

Common Values

ValueCountFrequency (%)
62+2870
 
2.3%
59+2824
 
2.2%
63+2822
 
2.2%
61+2821
 
2.2%
60+2809
 
2.2%
58+2804
 
2.1%
64+2776
 
2.1%
57+2745
 
2.0%
65+2714
 
1.9%
66+2706
 
1.9%
Other values (188)25465
67.8%
(Missing)4192
 
11.2%

Length

2022-01-13T14:14:19.283683image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
62+2870
 
2.6%
59+2824
 
2.5%
63+2822
 
2.5%
61+2821
 
2.5%
60+2809
 
2.4%
58+2804
 
2.4%
64+2776
 
2.3%
57+2745
 
2.2%
65+2714
 
2.1%
66+2706
 
2.1%
Other values (188)25465
76.3%

Most occurring characters

ValueCountFrequency (%)
+33356
25.0%
225526
19.1%
614344
10.8%
513709
10.3%
113472
10.1%
410052
 
7.5%
76740
 
5.1%
35595
 
4.2%
03748
 
2.8%
83552
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number100068
75.0%
Math Symbol33356
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
225526
25.5%
614344
14.3%
513709
13.7%
113472
13.5%
410052
 
10.0%
76740
 
6.7%
35595
 
5.6%
03748
 
3.7%
83552
 
3.5%
93330
 
3.3%
Math Symbol
ValueCountFrequency (%)
+33356
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common133424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
+33356
25.0%
225526
19.1%
614344
10.8%
513709
10.3%
113472
10.1%
410052
 
7.5%
76740
 
5.1%
35595
 
4.2%
03748
 
2.8%
83552
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII133424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
+33356
25.0%
225526
19.1%
614344
10.8%
513709
10.3%
113472
10.1%
410052
 
7.5%
76740
 
5.1%
35595
 
4.2%
03748
 
2.8%
83552
 
2.7%

rdm
Categorical

HIGH CARDINALITY
MISSING

Distinct198
Distinct (%)0.6%
Missing4192
Missing (%)11.2%
Memory size2.1 MiB
62+2
 
870
59+2
 
824
63+2
 
822
61+2
 
821
60+2
 
809
Other values (193)
29210 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters133424
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)< 0.1%

Sample

1st row74+2
2nd row71+2
3rd row60+2
4th row46+2
5th row57+1

Common Values

ValueCountFrequency (%)
62+2870
 
2.3%
59+2824
 
2.2%
63+2822
 
2.2%
61+2821
 
2.2%
60+2809
 
2.2%
58+2804
 
2.1%
64+2776
 
2.1%
57+2745
 
2.0%
65+2714
 
1.9%
66+2706
 
1.9%
Other values (188)25465
67.8%
(Missing)4192
 
11.2%

Length

2022-01-13T14:14:19.387288image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
62+2870
 
2.6%
59+2824
 
2.5%
63+2822
 
2.5%
61+2821
 
2.5%
60+2809
 
2.4%
58+2804
 
2.4%
64+2776
 
2.3%
57+2745
 
2.2%
65+2714
 
2.1%
66+2706
 
2.1%
Other values (188)25465
76.3%

Most occurring characters

ValueCountFrequency (%)
+33356
25.0%
225526
19.1%
614344
10.8%
513709
10.3%
113472
10.1%
410052
 
7.5%
76740
 
5.1%
35595
 
4.2%
03748
 
2.8%
83552
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number100068
75.0%
Math Symbol33356
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
225526
25.5%
614344
14.3%
513709
13.7%
113472
13.5%
410052
 
10.0%
76740
 
6.7%
35595
 
5.6%
03748
 
3.7%
83552
 
3.5%
93330
 
3.3%
Math Symbol
ValueCountFrequency (%)
+33356
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common133424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
+33356
25.0%
225526
19.1%
614344
10.8%
513709
10.3%
113472
10.1%
410052
 
7.5%
76740
 
5.1%
35595
 
4.2%
03748
 
2.8%
83552
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII133424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
+33356
25.0%
225526
19.1%
614344
10.8%
513709
10.3%
113472
10.1%
410052
 
7.5%
76740
 
5.1%
35595
 
4.2%
03748
 
2.8%
83552
 
2.7%

rwb
Categorical

HIGH CARDINALITY
MISSING

Distinct188
Distinct (%)0.6%
Missing4192
Missing (%)11.2%
Memory size2.1 MiB
59+2
 
953
61+2
 
931
62+2
 
913
60+2
 
898
55+2
 
860
Other values (183)
28801 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters133424
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)0.1%

Sample

1st row71+2
2nd row71+2
3rd row60+2
4th row43+2
5th row56+1

Common Values

ValueCountFrequency (%)
59+2953
 
2.5%
61+2931
 
2.5%
62+2913
 
2.4%
60+2898
 
2.4%
55+2860
 
2.3%
57+2859
 
2.3%
54+2857
 
2.3%
63+2853
 
2.3%
56+2852
 
2.3%
53+2837
 
2.2%
Other values (178)24543
65.4%
(Missing)4192
 
11.2%

Length

2022-01-13T14:14:19.480366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
59+2953
 
2.9%
61+2931
 
2.8%
62+2913
 
2.7%
60+2898
 
2.7%
55+2860
 
2.6%
57+2859
 
2.6%
54+2857
 
2.6%
63+2853
 
2.6%
56+2852
 
2.6%
53+2837
 
2.5%
Other values (178)24543
73.6%

Most occurring characters

ValueCountFrequency (%)
+33356
25.0%
225535
19.1%
515578
11.7%
614744
11.1%
113547
10.2%
49037
 
6.8%
76329
 
4.7%
34725
 
3.5%
03864
 
2.9%
93373
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number100068
75.0%
Math Symbol33356
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
225535
25.5%
515578
15.6%
614744
14.7%
113547
13.5%
49037
 
9.0%
76329
 
6.3%
34725
 
4.7%
03864
 
3.9%
93373
 
3.4%
83336
 
3.3%
Math Symbol
ValueCountFrequency (%)
+33356
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common133424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
+33356
25.0%
225535
19.1%
515578
11.7%
614744
11.1%
113547
10.2%
49037
 
6.8%
76329
 
4.7%
34725
 
3.5%
03864
 
2.9%
93373
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII133424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
+33356
25.0%
225535
19.1%
515578
11.7%
614744
11.1%
113547
10.2%
49037
 
6.8%
76329
 
4.7%
34725
 
3.5%
03864
 
2.9%
93373
 
2.5%

lb
Categorical

HIGH CARDINALITY
MISSING

Distinct188
Distinct (%)0.6%
Missing4192
Missing (%)11.2%
Memory size2.1 MiB
61+2
 
912
63+2
 
898
59+2
 
878
64+2
 
851
62+2
 
847
Other values (183)
28970 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters133424
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)< 0.1%

Sample

1st row69+2
2nd row70+2
3rd row58+2
4th row41+2
5th row57+1

Common Values

ValueCountFrequency (%)
61+2912
 
2.4%
63+2898
 
2.4%
59+2878
 
2.3%
64+2851
 
2.3%
62+2847
 
2.3%
60+2835
 
2.2%
58+2829
 
2.2%
57+2789
 
2.1%
65+2788
 
2.1%
56+2762
 
2.0%
Other values (178)24967
66.5%
(Missing)4192
 
11.2%

Length

2022-01-13T14:14:19.584052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
61+2912
 
2.7%
63+2898
 
2.7%
59+2878
 
2.6%
64+2851
 
2.6%
62+2847
 
2.5%
60+2835
 
2.5%
58+2829
 
2.5%
57+2789
 
2.4%
65+2788
 
2.4%
56+2762
 
2.3%
Other values (178)24967
74.9%

Most occurring characters

ValueCountFrequency (%)
+33356
25.0%
225527
19.1%
614599
10.9%
514224
10.7%
113572
10.2%
49932
 
7.4%
76337
 
4.7%
35315
 
4.0%
03772
 
2.8%
83423
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number100068
75.0%
Math Symbol33356
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
225527
25.5%
614599
14.6%
514224
14.2%
113572
13.6%
49932
 
9.9%
76337
 
6.3%
35315
 
5.3%
03772
 
3.8%
83423
 
3.4%
93367
 
3.4%
Math Symbol
ValueCountFrequency (%)
+33356
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common133424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
+33356
25.0%
225527
19.1%
614599
10.9%
514224
10.7%
113572
10.2%
49932
 
7.4%
76337
 
4.7%
35315
 
4.0%
03772
 
2.8%
83423
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII133424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
+33356
25.0%
225527
19.1%
614599
10.9%
514224
10.7%
113572
10.2%
49932
 
7.4%
76337
 
4.7%
35315
 
4.0%
03772
 
2.8%
83423
 
2.6%

lcb
Categorical

HIGH CARDINALITY
MISSING

Distinct210
Distinct (%)0.6%
Missing4192
Missing (%)11.2%
Memory size2.1 MiB
63+2
 
863
62+2
 
845
64+2
 
838
61+2
 
783
65+2
 
775
Other values (205)
29252 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters133424
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st row69+2
2nd row68+2
3rd row56+2
4th row44+2
5th row58+1

Common Values

ValueCountFrequency (%)
63+2863
 
2.3%
62+2845
 
2.3%
64+2838
 
2.2%
61+2783
 
2.1%
65+2775
 
2.1%
66+2720
 
1.9%
60+2694
 
1.8%
67+2671
 
1.8%
59+2651
 
1.7%
68+2637
 
1.7%
Other values (200)25879
68.9%
(Missing)4192
 
11.2%

Length

2022-01-13T14:14:19.678692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
63+2863
 
2.6%
62+2845
 
2.5%
64+2838
 
2.5%
61+2783
 
2.3%
65+2775
 
2.3%
66+2720
 
2.2%
60+2694
 
2.1%
67+2671
 
2.0%
59+2651
 
2.0%
68+2637
 
1.9%
Other values (200)25879
77.6%

Most occurring characters

ValueCountFrequency (%)
+33356
25.0%
225773
19.3%
614148
10.6%
113600
10.2%
510827
 
8.1%
410660
 
8.0%
37676
 
5.8%
77084
 
5.3%
03710
 
2.8%
83498
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number100068
75.0%
Math Symbol33356
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
225773
25.8%
614148
14.1%
113600
13.6%
510827
10.8%
410660
10.7%
37676
 
7.7%
77084
 
7.1%
03710
 
3.7%
83498
 
3.5%
93092
 
3.1%
Math Symbol
ValueCountFrequency (%)
+33356
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common133424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
+33356
25.0%
225773
19.3%
614148
10.6%
113600
10.2%
510827
 
8.1%
410660
 
8.0%
37676
 
5.8%
77084
 
5.3%
03710
 
2.8%
83498
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII133424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
+33356
25.0%
225773
19.3%
614148
10.6%
113600
10.2%
510827
 
8.1%
410660
 
8.0%
37676
 
5.8%
77084
 
5.3%
03710
 
2.8%
83498
 
2.6%

cb
Categorical

HIGH CARDINALITY
MISSING

Distinct210
Distinct (%)0.6%
Missing4192
Missing (%)11.2%
Memory size2.1 MiB
63+2
 
863
62+2
 
845
64+2
 
838
61+2
 
783
65+2
 
775
Other values (205)
29252 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters133424
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st row69+2
2nd row68+2
3rd row56+2
4th row44+2
5th row58+1

Common Values

ValueCountFrequency (%)
63+2863
 
2.3%
62+2845
 
2.3%
64+2838
 
2.2%
61+2783
 
2.1%
65+2775
 
2.1%
66+2720
 
1.9%
60+2694
 
1.8%
67+2671
 
1.8%
59+2651
 
1.7%
68+2637
 
1.7%
Other values (200)25879
68.9%
(Missing)4192
 
11.2%

Length

2022-01-13T14:14:19.778770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
63+2863
 
2.6%
62+2845
 
2.5%
64+2838
 
2.5%
61+2783
 
2.3%
65+2775
 
2.3%
66+2720
 
2.2%
60+2694
 
2.1%
67+2671
 
2.0%
59+2651
 
2.0%
68+2637
 
1.9%
Other values (200)25879
77.6%

Most occurring characters

ValueCountFrequency (%)
+33356
25.0%
225773
19.3%
614148
10.6%
113600
10.2%
510827
 
8.1%
410660
 
8.0%
37676
 
5.8%
77084
 
5.3%
03710
 
2.8%
83498
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number100068
75.0%
Math Symbol33356
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
225773
25.8%
614148
14.1%
113600
13.6%
510827
10.8%
410660
10.7%
37676
 
7.7%
77084
 
7.1%
03710
 
3.7%
83498
 
3.5%
93092
 
3.1%
Math Symbol
ValueCountFrequency (%)
+33356
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common133424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
+33356
25.0%
225773
19.3%
614148
10.6%
113600
10.2%
510827
 
8.1%
410660
 
8.0%
37676
 
5.8%
77084
 
5.3%
03710
 
2.8%
83498
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII133424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
+33356
25.0%
225773
19.3%
614148
10.6%
113600
10.2%
510827
 
8.1%
410660
 
8.0%
37676
 
5.8%
77084
 
5.3%
03710
 
2.8%
83498
 
2.6%

rcb
Categorical

HIGH CARDINALITY
MISSING

Distinct210
Distinct (%)0.6%
Missing4192
Missing (%)11.2%
Memory size2.1 MiB
63+2
 
863
62+2
 
845
64+2
 
838
61+2
 
783
65+2
 
775
Other values (205)
29252 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters133424
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st row69+2
2nd row68+2
3rd row56+2
4th row44+2
5th row58+1

Common Values

ValueCountFrequency (%)
63+2863
 
2.3%
62+2845
 
2.3%
64+2838
 
2.2%
61+2783
 
2.1%
65+2775
 
2.1%
66+2720
 
1.9%
60+2694
 
1.8%
67+2671
 
1.8%
59+2651
 
1.7%
68+2637
 
1.7%
Other values (200)25879
68.9%
(Missing)4192
 
11.2%

Length

2022-01-13T14:14:19.868674image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
63+2863
 
2.6%
62+2845
 
2.5%
64+2838
 
2.5%
61+2783
 
2.3%
65+2775
 
2.3%
66+2720
 
2.2%
60+2694
 
2.1%
67+2671
 
2.0%
59+2651
 
2.0%
68+2637
 
1.9%
Other values (200)25879
77.6%

Most occurring characters

ValueCountFrequency (%)
+33356
25.0%
225773
19.3%
614148
10.6%
113600
10.2%
510827
 
8.1%
410660
 
8.0%
37676
 
5.8%
77084
 
5.3%
03710
 
2.8%
83498
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number100068
75.0%
Math Symbol33356
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
225773
25.8%
614148
14.1%
113600
13.6%
510827
10.8%
410660
10.7%
37676
 
7.7%
77084
 
7.1%
03710
 
3.7%
83498
 
3.5%
93092
 
3.1%
Math Symbol
ValueCountFrequency (%)
+33356
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common133424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
+33356
25.0%
225773
19.3%
614148
10.6%
113600
10.2%
510827
 
8.1%
410660
 
8.0%
37676
 
5.8%
77084
 
5.3%
03710
 
2.8%
83498
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII133424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
+33356
25.0%
225773
19.3%
614148
10.6%
113600
10.2%
510827
 
8.1%
410660
 
8.0%
37676
 
5.8%
77084
 
5.3%
03710
 
2.8%
83498
 
2.6%

rb
Categorical

HIGH CARDINALITY
MISSING

Distinct188
Distinct (%)0.6%
Missing4192
Missing (%)11.2%
Memory size2.1 MiB
61+2
 
912
63+2
 
898
59+2
 
878
64+2
 
851
62+2
 
847
Other values (183)
28970 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters133424
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)< 0.1%

Sample

1st row69+2
2nd row70+2
3rd row58+2
4th row41+2
5th row57+1

Common Values

ValueCountFrequency (%)
61+2912
 
2.4%
63+2898
 
2.4%
59+2878
 
2.3%
64+2851
 
2.3%
62+2847
 
2.3%
60+2835
 
2.2%
58+2829
 
2.2%
57+2789
 
2.1%
65+2788
 
2.1%
56+2762
 
2.0%
Other values (178)24967
66.5%
(Missing)4192
 
11.2%

Length

2022-01-13T14:14:19.962342image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
61+2912
 
2.7%
63+2898
 
2.7%
59+2878
 
2.6%
64+2851
 
2.6%
62+2847
 
2.5%
60+2835
 
2.5%
58+2829
 
2.5%
57+2789
 
2.4%
65+2788
 
2.4%
56+2762
 
2.3%
Other values (178)24967
74.9%

Most occurring characters

ValueCountFrequency (%)
+33356
25.0%
225527
19.1%
614599
10.9%
514224
10.7%
113572
10.2%
49932
 
7.4%
76337
 
4.7%
35315
 
4.0%
03772
 
2.8%
83423
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number100068
75.0%
Math Symbol33356
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
225527
25.5%
614599
14.6%
514224
14.2%
113572
13.6%
49932
 
9.9%
76337
 
6.3%
35315
 
5.3%
03772
 
3.8%
83423
 
3.4%
93367
 
3.4%
Math Symbol
ValueCountFrequency (%)
+33356
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common133424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
+33356
25.0%
225527
19.1%
614599
10.9%
514224
10.7%
113572
10.2%
49932
 
7.4%
76337
 
4.7%
35315
 
4.0%
03772
 
2.8%
83423
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII133424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
+33356
25.0%
225527
19.1%
614599
10.9%
514224
10.7%
113572
10.2%
49932
 
7.4%
76337
 
4.7%
35315
 
4.0%
03772
 
2.8%
83423
 
2.6%

Interactions

2022-01-13T14:13:28.338861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:28.513774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:28.730003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:28.779291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:28.833583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:28.886186image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:28.939183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:28.987896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:29.035214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:29.086197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:29.133090image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:29.188793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:29.239866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:29.289455image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:29.338848image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:29.388076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:29.442870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:29.490097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:29.535469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:29.604238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:29.667099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:29.750445image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:29.823806image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:29.879183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:29.941084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:30.003350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:30.062426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:30.117649image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:30.171561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:30.649419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:30.769067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:30.859080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:30.944748image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:31.001577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:31.131076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:31.186314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:31.278762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:31.368208image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:31.496018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:31.559351image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:31.607373image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:31.659431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:31.710576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:31.762211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:31.811273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:31.859188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:31.912290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:31.965684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:32.012944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:32.065744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:32.113090image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:32.158605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:32.204513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:32.252567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:32.299127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:32.360787image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:32.424337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:32.479719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:32.535211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:32.584782image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:32.636355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:32.686443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:32.771139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:33.052130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:33.104369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:33.157114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:33.204782image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:33.252731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:33.307161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:33.355569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:33.401384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:33.450301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:33.494708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:33.543486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:33.587365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:33.633172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:33.684247image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:33.731029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:33.781404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:33.826425image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:33.869658image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:33.919372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:33.966159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:34.017292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:34.075621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:34.129769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:34.177375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:34.233996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:34.281209image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:34.332018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:34.380561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:34.429204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:34.490059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:34.545557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:34.600096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:34.644929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:34.694914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:34.740882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:34.785431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:34.830778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:34.876651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:34.935232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:34.987283image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:35.047234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:35.391880image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:35.449568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:35.508324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:35.561408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:35.621301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:35.665347image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:35.711522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:35.753053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:35.796831image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:35.841904image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:35.885898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:35.936698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:35.986757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:36.036863image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:36.080454image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:36.127254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:36.171059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:36.220620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:36.276579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:36.351847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:36.400945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:36.443822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:36.495661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:36.543060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:36.589636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:36.636813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:36.683000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:36.730311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:36.784913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:36.835798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:36.880303image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:36.922171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:36.966488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:37.009825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:37.056579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:37.105609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:37.158345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:37.221159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:37.274386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:37.341704image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:37.388801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:37.443772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:37.494135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:37.547907image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:37.594913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:37.649308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:37.761065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:37.815038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:37.867002image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-13T14:13:37.919823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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Correlations

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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
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Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
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Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
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Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

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Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
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The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
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The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

sofifa_idplayer_urlshort_namelong_nameagedobheight_cmweight_kgnationalitycluboverallpotentialvalueplayer_positionspreferred_footinternational_reputationweak_footskill_moveswork_ratebody_typereal_facerelease_clause_eurplayer_tagsteam_positionteam_jersey_numberloaned_fromjoinedcontract_valid_untilnation_positionnation_jersey_numberpaceshootingpassingdribblingdefendingphysicgk_divinggk_handlinggk_kickinggk_reflexesgk_speedgk_positioningplayer_traitsattacking_crossingattacking_finishingattacking_heading_accuracyattacking_short_passingattacking_volleysskill_dribblingskill_curveskill_fk_accuracyskill_long_passingskill_ball_controlmovement_accelerationmovement_sprint_speedmovement_agilitymovement_reactionsmovement_balancepower_shot_powerpower_jumpingpower_staminapower_strengthpower_long_shotsmentality_aggressionmentality_interceptionsmentality_positioningmentality_visionmentality_penaltiesmentality_composuredefending_markingdefending_standing_tackledefending_sliding_tacklegoalkeeping_divinggoalkeeping_handlinggoalkeeping_kickinggoalkeeping_positioninggoalkeeping_reflexeslsstrslwlfcfrfrwlamcamramlmlcmcmrcmrmlwbldmcdmrdmrwblblcbcbrcbrb
0216393https://sofifa.com/player/216393/youri-tielemans/19/159222Y. TielemansYouri Tielemans211997-05-0717672BelgiumAS Monaco798920000000CM, CDMRight254Medium/MediumNormalYes42000000.0#Distance ShooterRDM8.0NaN2017-07-012022.0SUB17.053.079.080.077.068.070.0NaNNaNNaNNaNNaNNaNLong Passer (CPU AI Only), Long Shot Taker (CPU AI Only), Playmaker (CPU AI Only)76735780787684748081525367757688657369-2887071748376816672696810141273+273+273+274+275+275+275+274+276+276+276+274+278+278+278+274+271+274+274+274+271+269+269+269+269+269+2
1187878https://sofifa.com/player/187878/lukas-marecek/19/159222L. MarečekLukáš Mareček281990-04-1718379Czech RepublicSporting Lokeren71712400000CM, CDM, CAMRight133Medium/MediumLeanNo3500000.0NaNLDM23.0NaN2018-01-292021.0NaNNaN70.057.070.069.069.071.0NaNNaNNaNNaNNaNNaNAvoids Using Weaker Foot, Early Crosser68486072-35767696869-27469716967-265746882656372-470-557725568-26972-168-2891114963+263+263+266+265+265+265+266+267+267+267+268+269+269+269+268+271+271+271+271+271+270+268+268+268+270+2
2178628https://sofifa.com/player/178628/fernando-forestieri/20/159586F. ForestieriFernando Martín Forestieri291990-01-1517267ItalySheffield Wednesday73734000000ST, CAMRight134High/MediumNormalNo7600000.0#AcrobatSUB45.0NaN2015-08-292021.0NaNNaN78.073.068.079.048.067.0NaNNaNNaNNaNNaNNaNSelfish, Argues with Officials, Crowd Favourite, Skilled Dribbling647359727178706560778274907088758468637373427073677150533166712871+271+271+274+273+273+273+274+273+273+273+272+268+268+268+272+260+260+260+260+260+258+256+256+256+258+2
3184341https://sofifa.com/player/184341/riccardo-maniero/20/159586R. ManieroRiccardo Maniero311987-11-2618375ItalyPescara6969950000STRight132High/LowNormalNo1500000.0NaNST19.0NaN2019-07-182022.0NaNNaN52.070.054.063.032.071.0NaNNaNNaNNaNNaNNaNNaN4072736262596245526954517360567082628165522073557266451813811991267+267+267+261+264+264+264+261+262+262+262+259+257+257+257+259+243+246+246+246+243+241+244+244+244+241+2
4231933https://sofifa.com/player/231933/rowllin-borges/18/158855R. BorgesRowllin Borges251992-06-0518575IndiaIndia58640CDMRight132Medium/MediumLeanNoNaNNaNNaNNaNNaNNaNNaNLDM14.057.035.049.050.057.064.0NaNNaNNaNNaNNaNNaNNaN413355543145333956545857545959445266682855583947404058565710765647+147+147+148+148+148+148+148+149+149+149+150+152+152+152+150+156+157+157+157+156+157+158+158+158+157+1
5233670https://sofifa.com/player/233670/akito-fukumori/20/159586A. Fukumori福森 晃斗261992-12-1618375JapanHokkaido Consadole Sapporo6770850000CB, LB, CMLeft132Medium/HighNormalNo1100000.0NaNLCB5.0NaN2017-01-072022.0NaNNaN64.063.068.056.066.072.0NaNNaNNaNNaNNaNNaNNaN6563537237467274666866636466626968777768526442626265697065131087560+260+260+260+260+260+260+260+261+261+261+261+264+264+264+261+266+267+267+267+266+266+265+265+265+266+2
6101880https://sofifa.com/player/101880/rob-green/19/159222R. GreenRobert Green381980-01-1819193EnglandChelsea7272210000GKRight231Medium/MediumNormalYes389000.0NaNRES31.0NaN2018-07-262019.0NaNNaNNaNNaNNaNNaNNaNNaN73.070.060.074.038.072.0NaN19121526121416131415463247704022682659112620135821491412117370607274NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
7243972https://sofifa.com/player/243972/jordi-sanchez-ribas/19/159222Jordi SánchezJordi Sánchez Ribas231994-11-1119081SpainCD Numancia6270450000STRight132Medium/MediumLeanNo810000.0NaNRES20.0NaN2018-07-022021.0NaNNaN61.062.043.056.026.058.0NaNNaNNaNNaNNaNNaNNaN336764555357333226556061545848626056725327156349564936171010712111361+261+261+255+258+258+258+255+255+255+255+253+247+247+247+253+239+237+237+237+239+237+237+237+237+237+2
8232837https://sofifa.com/player/232837/ali-suljic/19/159222A. SuljicAli Suljic201997-09-1818979SwedenBK Häcken5769160000CBRight122Medium/MediumNormalNo232000.0NaNRES16.0NaN2018-07-022019.0NaNNaN58.028.039.038.055.062.0NaNNaNNaNNaNNaNNaNPower Header34255950262830214748556048455337625667225651292436435755551410771340+240+240+238+237+237+237+238+238+238+238+240+241+241+241+240+249+251+251+251+249+251+255+255+255+251+2
9202166https://sofifa.com/player/202166/julian-draxler/18/158855J. DraxlerJulian Draxler231993-09-2018772GermanyParis Saint-Germain848739500000LW, CAM, LMRight354Medium/LowLeanYes76000000.0#DribblerSUB23.0NaN2017-01-032021.0LM11.078.080.082.086.034.064.0NaNNaNNaNNaNNaNNaNFinesse Shot, Flair, Playmaker (CPU AI Only), Technical Dribbler (CPU AI Only)83+578+36584+18488+17875+377+587+276-179+281-183+27586+27668-1678148367984+46381+32535261113513880+180+180+183+183+183+183+183+184+184+184+183+178+178+178+183+162+160+160+160+162+158+149+149+149+158+1

Last rows

sofifa_idplayer_urlshort_namelong_nameagedobheight_cmweight_kgnationalitycluboverallpotentialvalueplayer_positionspreferred_footinternational_reputationweak_footskill_moveswork_ratebody_typereal_facerelease_clause_eurplayer_tagsteam_positionteam_jersey_numberloaned_fromjoinedcontract_valid_untilnation_positionnation_jersey_numberpaceshootingpassingdribblingdefendingphysicgk_divinggk_handlinggk_kickinggk_reflexesgk_speedgk_positioningplayer_traitsattacking_crossingattacking_finishingattacking_heading_accuracyattacking_short_passingattacking_volleysskill_dribblingskill_curveskill_fk_accuracyskill_long_passingskill_ball_controlmovement_accelerationmovement_sprint_speedmovement_agilitymovement_reactionsmovement_balancepower_shot_powerpower_jumpingpower_staminapower_strengthpower_long_shotsmentality_aggressionmentality_interceptionsmentality_positioningmentality_visionmentality_penaltiesmentality_composuredefending_markingdefending_standing_tackledefending_sliding_tacklegoalkeeping_divinggoalkeeping_handlinggoalkeeping_kickinggoalkeeping_positioninggoalkeeping_reflexeslsstrslwlfcfrfrwlamcamramlmlcmcmrcmrmlwbldmcdmrdmrwblblcbcbrcbrb
37538187176https://sofifa.com/player/187176/yun-pyo-lee/18/158855Lee Yun Pyo이윤표 李云彪321984-09-0418479Korea RepublicIncheon United FC6767450000CBRight132Low/HighLeanNo563000.0NaNLCB16.0NaN2011-01-012020.0NaNNaN47.047.046.060.068.072.0NaNNaNNaNNaNNaNNaNNaN433970495158443841595045686066617873745766641350516070686781312111350+150+150+149+149+149+149+149+150+150+150+150+153+153+153+150+160+161+161+161+160+161+167+167+167+161+1
37539192688https://sofifa.com/player/192688/athanasios-papazoglou/18/158855A. PapazoglouAthanasios Papazoglou291988-03-3019592GreeceAalesunds FK6666650000STRight133Medium/MediumNormalNoNaN#Aerial Threat, #StrengthLS10.0KV KortrijkNaN2017.0NaNNaN38.066.053.055.024.078.0NaNNaNNaNNaNNaNNaNTarget Forward316675636355535842634037396531735864916169236762615622111516118141365+165+165+156+162+162+162+156+160+160+160+155+156+156+156+155+140+145+145+145+140+138+143+143+143+138+1
37540193155https://sofifa.com/player/193155/lucas-musculus/19/159222L. MusculusLucas Musculus271991-01-1618073GermanyKFC Uerdingen 056363400000ST, CFRight132Medium/MediumLeanNo580000.0NaNSUB19.0NaN2017-07-142019.0NaNNaN68.063.045.062.027.057.0NaNNaNNaNNaNNaNNaNNaN376758486060575438627264726170614246725737246346636226202291614151262+262+262+258+260+260+260+258+257+257+257+255+249+249+249+255+241+239+239+239+241+240+238+238+238+240+2
37541196069https://sofifa.com/player/196069/jose-pedro-fuenzalida/19/159222J. FuenzalidaJosé Pedro Fuenzalida331985-02-2217067ChileUniversidad Católica74743400000RW, RBRight143High/HighNormalNo4800000.0NaNSUB19.0NaN2016-01-102019.0NaNNaN84.063.071.078.064.070.0NaNNaNNaNNaNNaNNaNSpeed Dribbler (CPU AI Only), Set Play Specialist765963725779655968788285777680697178706259687171716958666561412141270+270+270+274+273+273+273+274+273+273+273+275+272+272+272+275+272+269+269+269+272+271+266+266+266+271+2
37542227785https://sofifa.com/player/227785/cristian-guanca/20/159586C. GuancaCristian Guanca261993-03-2617878ArgentinaAl Shabab72753900000ST, CAMLeft133High/MediumNormalNo6800000.0NaNST10.0NaN2019-07-012022.0NaNNaN78.072.061.072.037.068.0NaNNaNNaNNaNNaNNaNArgues with Officials, Speed Dribbler (CPU AI Only)6076596759734640577179787565687367747169543672655968343533912781670+270+270+270+271+271+271+270+269+269+269+269+264+264+264+269+256+253+253+253+256+253+248+248+248+253+2
37543242308https://sofifa.com/player/242308/ibrahim-chenihi/19/159222I. ChenihiIbrahim Chenihi281990-01-2417775AlgeriaAl Fateh6262300000LM, CAMLeft133High/MediumLeanNo495000.0NaNLM24.0NaN2018-01-272019.0NaNNaN71.051.063.062.033.056.0NaNNaNNaNNaNNaNNaNNaN60573868386060576564687465487144595856455235555860522140406111214954+254+254+260+258+258+258+260+260+260+260+261+258+258+258+261+251+250+250+250+251+249+242+242+242+249+2
37544186259https://sofifa.com/player/186259/amiran-sanaia/20/159586A. SanaiaAmiran Sanaia291989-09-0318170GeorgiaRodez Aveyron Football6666525000LB, CBRight122Medium/HighNormalNo919000.0NaNLCB13.0NaN2017-08-172020.0NaNNaN59.038.058.056.065.072.0NaNNaNNaNNaNNaNNaNAvoids Using Weaker Foot6038626717515859566154636059694373787034676443434856656668119961350+250+250+253+251+251+251+253+252+252+252+256+256+256+256+256+264+263+263+263+264+264+265+265+265+264+2
37545218623https://sofifa.com/player/218623/carlos-miguel-ribeiro-dias/18/158855CafúCarlos Miguel Ribeiro Dias241993-02-2618384PortugalFC Metz73784500000CDM, CMRight132Medium/HighStockyNo9400000.0NaNLDM26.0NaN2017-08-012020.0NaNNaN66.070.061.068.071.085.0NaNNaNNaNNaNNaNNaNNaN5062717270-269-154466771686456+171-1648460+190+785+1738569685469-164-169-273-371-311913121171+171+171+166+169+169+169+166+167+167+167+167+170+170+170+167+170+173+173+173+170+170+173+173+173+170+1
37546244469https://sofifa.com/player/244469/pietro-di-nardo/19/159222P. Di NardoPietro Di Nardo281990-02-0817571SwitzerlandNeuchâtel Xamax5960140000CDM, CMRight132Medium/MediumNormalNo214000.0NaNRCM4.0NaN2014-07-042019.0NaNNaN56.040.054.056.057.062.0NaNNaNNaNNaNNaNNaNNaN5540586330533436565555566756744567696136545537444749555958910109748+248+248+251+249+249+249+251+251+251+251+253+253+253+253+253+257+257+257+257+257+257+257+257+257+257+2
37547236999https://sofifa.com/player/236999/david-guzman/19/159222D. GuzmanDavid Guzman281990-02-1817879Costa RicaPortland Timbers72732800000CDMRight133Medium/HighNormalYes4500000.0NaNSUB20.0NaN2016-12-222018.0NaNNaN63.059.071.068.067.074.0NaNNaNNaNNaNNaNNaNLong Passer (CPU AI Only), Long Shot Taker (CPU AI Only)70516172516770537568646268726571687868648368657261717066621071213863+263+263+266+265+265+265+266+267+267+267+268+270+270+270+268+268+270+270+270+268+267+267+267+267+267+2